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inproceedings | attardi-etal-2017-fa3l | {FA}3{L} at {S}em{E}val-2017 Task 3: A {T}h{R}ee Embeddings Recurrent Neural Network for Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2048/ | Attardi, Giuseppe and Carta, Antonio and Errica, Federico and Madotto, Andrea and Pannitto, Ludovica | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 299--304 | In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results. | null | null | 10.18653/v1/S17-2048 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,142 |
inproceedings | qi-etal-2017-scir | {SCIR}-{QA} at {S}em{E}val-2017 Task 3: {CNN} Model Based on Similar and Dissimilar Information between Keywords for Question Similarity | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2049/ | Qi, Le and Zhang, Yu and Liu, Ting | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 305--309 | We describe a method of calculating the similarity of questions in community QA. Question in cQA are usually very long and there are a lot of useless information about calculating the similarity of questions. Therefore,we implement a CNN model based on similar and dissimilar information between question`s keywords. We extract the keywords of questions, and then model the similar and dissimilar information between the keywords, and use the CNN model to calculate the similarity. | null | null | 10.18653/v1/S17-2049 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,143 |
inproceedings | goyal-2017-learningtoquestion | {L}earning{T}o{Q}uestion at {S}em{E}val 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2050/ | Goyal, Naman | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 310--314 | This paper describes our official entry LearningToQuestion for SemEval 2017 task 3 community question answer, subtask B. The objective is to rerank questions obtained in web forum as per their similarity to original question. Our system uses pairwise learning to rank methods on rich set of hand designed and representation learning features. We use various semantic features that help our system to achieve promising results on the task. The system achieved second highest results on official metrics MAP and good results on other search metrics. | null | null | 10.18653/v1/S17-2050 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,144 |
inproceedings | charlet-damnati-2017-simbow-semeval | {S}im{B}ow at {S}em{E}val-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2051/ | Charlet, Delphine and Damnati, G{\'e}raldine | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 315--319 | This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a soft-cosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B. | null | null | 10.18653/v1/S17-2051 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,145 |
inproceedings | zhang-etal-2017-furongwang | {F}u{R}ong{W}ang at {S}em{E}val-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2052/ | Zhang, Sheng and Cheng, Jiajun and Wang, Hui and Zhang, Xin and Li, Pei and Ding, Zhaoyun | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 320--325 | We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C. | null | null | 10.18653/v1/S17-2052 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,146 |
inproceedings | filice-etal-2017-kelp | {K}e{LP} at {S}em{E}val-2017 Task 3: Learning Pairwise Patterns in Community Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2053/ | Filice, Simone and Da San Martino, Giovanni and Moschitti, Alessandro | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 326--333 | This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team`s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs. | null | null | 10.18653/v1/S17-2053 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,147 |
inproceedings | deriu-cieliebak-2017-swissalps | {S}wiss{A}lps at {S}em{E}val-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2054/ | Deriu, Jan Milan and Cieliebak, Mark | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 334--338 | In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86:24 points on the Question-Comment Similarity subtask. | null | null | 10.18653/v1/S17-2054 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,148 |
inproceedings | saina-etal-2017-takelab | {T}ake{L}ab-{QA} at {S}em{E}val-2017 Task 3: Classification Experiments for Answer Retrieval in Community {QA} | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2055/ | {\v{S}}aina, Filip and Kukurin, Toni and Pulji{\'c}, Lukrecija and Karan, Vanja Mladen and {\v{S}}najder, Jan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 339--343 | In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, including two supervised learning models {--} Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We use features based on different semantic similarity models (e.g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embeddings. Moreover, we also use some hand-crafted task-specific features. For training, our system uses no external labeled data apart from that provided by the organizers. Our primary submission achieves a MAP-score of 81.14 and F1-score of 66.99 {--} ranking us 10th on the SemEval 2017 task 3, subtask A. | null | null | 10.18653/v1/S17-2055 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,149 |
inproceedings | almarwani-diab-2017-gw | {GW}{\_}{QA} at {S}em{E}val-2017 Task 3: Question Answer Re-ranking on {A}rabic Fora | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2056/ | Almarwani, Nada and Diab, Mona | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 344--348 | This paper describes our submission to SemEval-2017 Task 3 Subtask D, {\textquotedblleft}Question Answer Ranking in Arabic Community Question Answering{\textquotedblright}. In this work, we applied a supervised machine learning approach to automatically re-rank a set of QA pairs according to their relevance to a given question. We employ features based on latent semantic models, namely WTMF, as well as a set of lexical features based on string lengths and surface level matching. The proposed system ranked first out of 3 submissions, with a MAP score of 61.16{\%}. | null | null | 10.18653/v1/S17-2056 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,150 |
inproceedings | ben-abacha-demner-fushman-2017-nlm | {NLM}{\_}{NIH} at {S}em{E}val-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2057/ | Ben Abacha, Asma and Demner-Fushman, Dina | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 349--352 | This paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a search engine according to their similarity to the original question. We adapted our feature-based system for Recognizing Question Entailment (RQE) to the question similarity task. Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77.47 MAP and 80.57 Accuracy. On cQA-B-2017 test data, performances of all systems dropped by around 30 points. Our primary system obtained 44.62 MAP, 67.27 Accuracy and 47.25 F1 score. The cQA-B-2017 best system achieved 47.22 MAP and 42.37 F1 score. Our system is ranked sixth in terms of MAP and third in terms of F1 out of 13 participating teams. | null | null | 10.18653/v1/S17-2057 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,151 |
inproceedings | koreeda-etal-2017-bunji | bunji at {S}em{E}val-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2058/ | Koreeda, Yuta and Hashito, Takuya and Niwa, Yoshiki and Sato, Misa and Yanase, Toshihiko and Kurotsuchi, Kenzo and Yanai, Kohsuke | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 353--359 | This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combines neural similarity features and hand-crafted comment plausibility features, and we modeled inter-comments relationship using conditional random field. Our approach obtained the fifth place in the Subtask A and the second place in the Subtask C. | null | null | 10.18653/v1/S17-2058 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,152 |
inproceedings | torki-etal-2017-qu | {QU}-{BIGIR} at {S}em{E}val 2017 Task 3: Using Similarity Features for {A}rabic Community Question Answering Forums | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2059/ | Torki, Marwan and Hasanain, Maram and Elsayed, Tamer | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 360--364 | In this paper we describe our QU-BIGIR system for the Arabic subtask D of the SemEval 2017 Task 3. Our approach builds on our participation in the past version of the same subtask. This year, our system uses different similarity measures that encodes lexical and semantic pairwise similarity of text pairs. In addition to well known similarity measures such as cosine similarity, we use other measures based on the summary statistics of word embedding representation for a given text. To rank a list of candidate question answer pairs for a given question, we learn a linear SVM classifier over our similarity features. Our best resulting run came second in subtask D with a very competitive performance to the first-ranking system. | null | null | 10.18653/v1/S17-2059 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,153 |
inproceedings | wu-etal-2017-ecnu | {ECNU} at {S}em{E}val-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2060/ | Wu, Guoshun and Sheng, Yixuan and Lan, Man and Wu, Yuanbin | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 365--369 | This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i.e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity. For subtask A, we combined two different methods to represent question-comment pair, i.e., supervised model using traditional features and Convolutional Neural Network. For subtask B, we utilized the information of snippets returned from Search Engine with question subject as query. For subtask C, we ranked the comments by multiplying the probability of the pair related question comment being Good by the reciprocal rank of the related question. | null | null | 10.18653/v1/S17-2060 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,154 |
inproceedings | agustian-takamura-2017-uinsuska | {UINSUSKA}-{T}i{T}ech at {S}em{E}val-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for {CQA} | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2061/ | Agustian, Surya and Takamura, Hiroya | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 370--374 | The majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in subtask B) based on word embeddings. We exploit words importance levels in sentences or questions for similarity features, for classification and ranking with machine learning. Using only 2 types of similarity metric, our proposed method has shown comparable results with other complex systems. This method on subtask B 2017 dataset is ranked on position 7 out of 13 participants. Evaluation on 2016 dataset is on position 8 of 12, outperforms some complex systems. Further, this finding is explorable and potential to be used as baseline and extensible for many tasks in CQA and other textual similarity based system. | null | null | 10.18653/v1/S17-2061 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,155 |
inproceedings | galbraith-etal-2017-talla | Talla at {S}em{E}val-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2062/ | Galbraith, Byron and Pratap, Bhanu and Shank, Daniel | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 375--379 | This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set. | null | null | 10.18653/v1/S17-2062 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,156 |
inproceedings | han-toner-2017-qub | {QUB} at {S}em{E}val-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2063/ | Han, Xiwu and Toner, Gregory | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 380--384 | This paper presents our submission to SemEval-2017 Task 6: {\#HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Na{\"ive Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B. | null | null | 10.18653/v1/S17-2063 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,157 |
inproceedings | yan-pedersen-2017-duluth | {D}uluth at {S}em{E}val-2017 Task 6: Language Models in Humor Detection | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2064/ | Yan, Xinru and Pedersen, Ted | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 385--389 | This paper describes the Duluth system that participated in SemEval-2017 Task 6 {\#}HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation runs. | null | null | 10.18653/v1/S17-2064 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,158 |
inproceedings | baziotis-etal-2017-datastories | {D}ata{S}tories at {S}em{E}val-2017 Task 6: {S}iamese {LSTM} with Attention for Humorous Text Comparison | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2065/ | Baziotis, Christos and Pelekis, Nikos and Doulkeridis, Christos | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 390--395 | In this paper we present a deep-learning system that competed at SemEval-2017 Task 6 ''{\#}HashtagWars: Learning a Sense of Humor{\textquotedblright}. We participated in Subtask A, in which the goal was, given two Twitter messages, to identify which one is funnier. We propose a Siamese architecture with bidirectional Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism. Our system works on the token-level, leveraging word embeddings trained on a big collection of unlabeled Twitter messages. We ranked 2nd in 7 teams. A post-completion improvement of our model, achieves state-of-the-art results on {\#}HashtagWars dataset. | null | null | 10.18653/v1/S17-2065 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,159 |
inproceedings | kukovacec-etal-2017-takelab | {T}ake{L}ab at {S}em{E}val-2017 Task 6: {\#}{R}anking{H}umor{I}n4{P}ages | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2066/ | Kukova{\v{c}}ec, Marin and Malenica, Juraj and Mr{\v{s}}i{\'c}, Ivan and {\v{S}}ajatovi{\'c}, Antonio and Alagi{\'c}, Domagoj and {\v{S}}najder, Jan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 396--400 | This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: {\#}HashtagWars (6A and 6B). For both subtasks, we use an off-the-shelf gradient boosting model built on a rich set of features, handcrafted to provide the model with the external knowledge needed to better predict the humor in the text. The features capture various cultural references and specific humor patterns. Our system ranked 2nd (officially 7th) among 10 submissions on the Subtask A and 2nd among 9 submissions on the Subtask B. | null | null | 10.18653/v1/S17-2066 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,160 |
inproceedings | cattle-ma-2017-srhr | {SRHR} at {S}em{E}val-2017 Task 6: Word Associations for Humour Recognition | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2067/ | Cattle, Andrew and Ma, Xiaojuan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 401--406 | This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examine a variety of word association features derived from University of Southern Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus (EAT) and find that word association-based features outperform Word2Vec similarity, a popular semantic relatedness measure. Our system achieves an accuracy of 56.42{\%} using a combination of unigram perplexity, bigram perplexity, EAT difference (tweet-avg), USF forward (max), EAT difference (word-avg), USF difference (word-avg), EAT forward (min), USF difference (tweet-max), and EAT backward (min). | null | null | 10.18653/v1/S17-2067 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,161 |
inproceedings | flescan-lovin-arseni-etal-2017-warteam | {\#}{W}ar{T}eam at {S}em{E}val-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2068/ | Fleșcan-Lovin-Arseni, Iuliana Alexandra and Turcu, Ramona Andreea and S{\^i}rbu, Cristina and Alexa, Larisa and Amarandei, Sandra Maria and Herciu, Nichita and Scutaru, Constantin and Trandab{\u{a}}ț, Diana and Iftene, Adrian | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 407--410 | This paper presents the participation of {\#WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. {\#WarTeam`s system uses a neural network (TensorFlow) having inputs from a Na{\"ive Bayes humor classifier and a sentiment analyzer. | null | null | 10.18653/v1/S17-2068 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,162 |
inproceedings | mahajan-zaveri-2017-svnit | {SVNIT} @ {S}em{E}val 2017 Task-6: Learning a Sense of Humor Using Supervised Approach | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2069/ | Mahajan, Rutal and Zaveri, Mukesh | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 411--415 | This paper describes the system devel-oped for SemEval 2017 task 6: {\#HashTagWars -Learning a Sense of Hu-mor. Learning to recognize sense of hu-mor is the important task for language understanding applications. Different set of features based on frequency of words, structure of tweets and semantics are used in this system to identify the presence of humor in tweets. Supervised machine learning approaches, Multilayer percep-tron and Na{\"ive Bayes are used to classify the tweets in to three level of sense of humor. For given Hashtag, the system finds the funniest tweet and predicts the amount of funniness of all the other tweets. In official submitted runs, we have achieved 0.506 accuracy using mul-tilayer perceptron in subtask-A and 0.938 distance in subtask-B. Using Na{\"ive bayes in subtask-B, the system achieved 0.949 distance. Apart from official runs, this system have scored 0.751 accuracy in subtask-A using SVM. But still there is a wide room for improvement in system. | null | null | 10.18653/v1/S17-2069 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,163 |
inproceedings | vechtomova-2017-uwaterloo | {UW}aterloo at {S}em{E}val-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2071/ | Vechtomova, Olga | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 421--425 | The paper presents a system for locating a pun word. The developed method calculates a score for each word in a pun, using a number of components, including its Inverse Document Frequency (IDF), Normalized Pointwise Mutual Information (NPMI) with other words in the pun text, its position in the text, part-of-speech and some syntactic features. The method achieved the best performance in the Heterographic category and the second best in the Homographic. Further analysis showed that IDF is the most useful characteristic, whereas the count of words with which the given word has high NPMI has a negative effect on performance. | null | null | 10.18653/v1/S17-2071 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,165 |
inproceedings | mikhalkova-karyakin-2017-punfields | {P}un{F}ields at {S}em{E}val-2017 Task 7: Employing {R}oget`s Thesaurus in Automatic Pun Recognition and Interpretation | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2072/ | Mikhalkova, Elena and Karyakin, Yuri | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 426--431 | The article describes a model of automatic interpretation of English puns, based on Roget`s Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition. | null | null | 10.18653/v1/S17-2072 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,166 |
inproceedings | sevgili-etal-2017-n | N-Hance at {S}em{E}val-2017 Task 7: A Computational Approach using Word Association for Puns | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2074/ | Sevgili, {\"Ozge and Ghotbi, Nima and Tekir, Selma | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 436--439 | This paper presents a system developed for SemEval-2017 Task 7, Detection and Interpretation of English Puns consisting of three subtasks; pun detection, pun location, and pun interpretation, respectively. The system stands on recognizing a distinctive word which has a high association with the pun in the given sentence. The intended humorous meaning of pun is identified through the use of this word. Our official results confirm the potential of this approach. | null | null | 10.18653/v1/S17-2074 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,168 |
inproceedings | hurtado-etal-2017-elirf | {EL}i{RF}-{UPV} at {S}em{E}val-2017 Task 7: Pun Detection and Interpretation | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2075/ | Hurtado, Llu{\'i}s-F. and Segarra, Encarna and Pla, Ferran and Carrasco, Pascual and Gonz{\'a}lez, Jos{\'e}-{\'A}ngel | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 440--443 | This paper describes the participation of ELiRF-UPV team at task 7 (subtask 2: homographic pun detection and subtask 3: homographic pun interpretation) of SemEval2017. Our approach is based on the use of word embeddings to find related words in a sentence and a version of the Lesk algorithm to establish relationships between synsets. The results obtained are in line with those obtained by the other participants and they encourage us to continue working on this problem. | null | null | 10.18653/v1/S17-2075 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,169 |
inproceedings | oele-evang-2017-buzzsaw | {B}uzz{S}aw at {S}em{E}val-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic {E}nglish Puns with Sense Embeddings | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2076/ | Oele, Dieke and Evang, Kilian | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 444--448 | This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledge-based Word Sense Disambiguation (WSD) method based on sense embeddings. Pun-based jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation. | null | null | 10.18653/v1/S17-2076 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,170 |
inproceedings | vadehra-2017-uwav | {UWAV} at {S}em{E}val-2017 Task 7: Automated feature-based system for locating puns | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2077/ | Vadehra, Ankit | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 449--452 | In this paper we describe our system created for SemEval-2017 Task 7: Detection and Interpretation of English Puns. We tackle subtask 1, pun detection, by leveraging features selected from sentences to design a classifier that can disambiguate between the presence or absence of a pun. We address subtask 2, pun location, by utilizing a decision flow structure that uses presence or absence of certain features to decide the next action. The results obtained by our system are encouraging, considering the simplicity of the system. We consider this system as a precursor for deeper exploration on efficient feature selection for pun detection. | null | null | 10.18653/v1/S17-2077 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,171 |
inproceedings | xiu-etal-2017-ecnu | {ECNU} at {S}em{E}val-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate {E}nglish Puns | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2078/ | Xiu, Yuhuan and Lan, Man and Wu, Yuanbin | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 453--456 | This paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and locate English puns respectively. For subtask 1, we presented a supervised system to determine whether or not a sentence contains a pun using similarity features calculated on sense vectors or cluster center vectors. For subtask 2, we established an unsupervised system to locate the pun by scoring each word in the sentence and we assumed that the word with the smallest score is the pun. | null | null | 10.18653/v1/S17-2078 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,172 |
inproceedings | indurthi-oota-2017-fermi | Fermi at {S}em{E}val-2017 Task 7: Detection and Interpretation of Homographic puns in {E}nglish Language | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2079/ | Indurthi, Vijayasaradhi and Oota, Subba Reddy | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 457--460 | This paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper we provide detailed description of the approach, as well as the results obtained in the task. Our models achieved a F1-score of 77.65{\%} for Subtask 1 and 52.15{\%} for Subtask 2. | null | null | 10.18653/v1/S17-2079 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,173 |
inproceedings | bahuleyan-vechtomova-2017-uwaterloo | {UW}aterloo at {S}em{E}val-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2080/ | Bahuleyan, Hareesh and Vechtomova, Olga | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 461--464 | This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval. | null | null | 10.18653/v1/S17-2080 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,174 |
inproceedings | chen-etal-2017-ikm | {IKM} at {S}em{E}val-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2081/ | Chen, Yi-Chin and Liu, Zhao-Yang and Kao, Hung-Yu | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 465--469 | This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B. | null | null | 10.18653/v1/S17-2081 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,175 |
inproceedings | kochkina-etal-2017-turing | {T}uring at {S}em{E}val-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-{LSTM} | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2083/ | Kochkina, Elena and Liakata, Maria and Augenstein, Isabelle | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 475--480 | This paper describes team Turing`s submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A. | null | null | 10.18653/v1/S17-2083 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,177 |
inproceedings | garcia-lozano-etal-2017-mama | Mama Edha at {S}em{E}val-2017 Task 8: Stance Classification with {CNN} and Rules | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2084/ | Garc{\'ia Lozano, Marianela and Lilja, Hanna and Tj{\"ornhammar, Edward and Karasalo, Maja | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 481--485 | For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9{\%} on the competition`s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification. | null | null | 10.18653/v1/S17-2084 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,178 |
inproceedings | srivastava-etal-2017-dfki | {DFKI}-{DKT} at {S}em{E}val-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2085/ | Srivastava, Ankit and Rehm, Georg and Moreno Schneider, Julian | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 486--490 | We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining veracity of a message, closed variant). In both cases, our implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules (heuristics) applied in a post-process cascading fashion. We provide a detailed analysis of the system performance and report on variants of our systems that were not part of the official submission. | null | null | 10.18653/v1/S17-2085 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,179 |
inproceedings | wang-etal-2017-ecnu | {ECNU} at {S}em{E}val-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2086/ | Wang, Feixiang and Lan, Man and Wu, Yuanbin | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 491--496 | This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A . | null | null | 10.18653/v1/S17-2086 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,180 |
inproceedings | singh-etal-2017-iitp | {IITP} at {S}em{E}val-2017 Task 8 : A Supervised Approach for Rumour Evaluation | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2087/ | Singh, Vikram and Narayan, Sunny and Akhtar, Md Shad and Ekbal, Asif and Bhattacharyya, Pushpak | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 497--501 | This paper describes our system participation in the SemEval-2017 Task 8 {\textquoteleft}RumourEval: Determining rumour veracity and support for rumours'. The objective of this task was to predict the stance and veracity of the underlying rumour. We propose a supervised classification approach employing several lexical, content and twitter specific features for learning. Evaluation shows promising results for both the problems. | null | null | 10.18653/v1/S17-2087 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,181 |
inproceedings | rosenthal-etal-2017-semeval | {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2088/ | Rosenthal, Sara and Farra, Noura and Nakov, Preslav | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 502--518 | This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year. | null | null | 10.18653/v1/S17-2088 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,182 |
inproceedings | cortis-etal-2017-semeval | {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2089/ | Cortis, Keith and Freitas, Andr{\'e} and Daudert, Tobias and Huerlimann, Manuela and Zarrouk, Manel and Handschuh, Siegfried and Davis, Brian | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 519--535 | This paper discusses the {\textquotedblleft}Fine-Grained Sentiment Analysis on Financial Microblogs and News{\textquotedblright} task as part of SemEval-2017, specifically under the {\textquotedblleft}Detecting sentiment, humour, and truth{\textquotedblright} theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2. | null | null | 10.18653/v1/S17-2089 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,183 |
inproceedings | may-priyadarshi-2017-semeval | {S}em{E}val-2017 Task 9: {A}bstract {M}eaning {R}epresentation Parsing and Generation | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2090/ | May, Jonathan and Priyadarshi, Jay | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 536--545 | In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants' systems, we show various score ablations and some sample outputs. | null | null | 10.18653/v1/S17-2090 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,184 |
inproceedings | augenstein-etal-2017-semeval | {S}em{E}val 2017 Task 10: {S}cience{IE} - Extracting Keyphrases and Relations from Scientific Publications | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2091/ | Augenstein, Isabelle and Das, Mrinal and Riedel, Sebastian and Vikraman, Lakshmi and McCallum, Andrew | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 546--555 | We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities. | null | null | 10.18653/v1/S17-2091 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,185 |
inproceedings | sales-etal-2017-semeval | {S}em{E}val-2017 Task 11: End-User Development using Natural Language | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2092/ | Sales, Juliano and Handschuh, Siegfried and Freitas, Andr{\'e} | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 556--564 | This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language. The task aims for supporting the evaluation and evolution of the discussions surrounding the natural language processing approaches within the context of end-user natural language programming, under scenarios of high semantic heterogeneity/gap. | null | null | 10.18653/v1/S17-2092 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,186 |
inproceedings | bethard-etal-2017-semeval | {S}em{E}val-2017 Task 12: Clinical {T}emp{E}val | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2093/ | Bethard, Steven and Savova, Guergana and Palmer, Martha and Pustejovsky, James | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 565--572 | Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer). | null | null | 10.18653/v1/S17-2093 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,187 |
inproceedings | cliche-2017-bb | {BB}{\_}twtr at {S}em{E}val-2017 Task 4: {T}witter Sentiment Analysis with {CNN}s and {LSTM}s | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2094/ | Cliche, Mathieu | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 573--580 | In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams. | null | null | 10.18653/v1/S17-2094 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,188 |
inproceedings | moore-rayson-2017-lancaster | {L}ancaster A at {S}em{E}val-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2095/ | Moore, Andrew and Rayson, Paul | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 581--585 | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6{\%} using the LSTM model over the SVR and came fourth in the track. We report a number of different evaluations using a finance specific word embedding model and reflect on the effects of using different evaluation metrics. | null | null | 10.18653/v1/S17-2095 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,189 |
inproceedings | lampouras-vlachos-2017-sheffield | {S}heffield at {S}em{E}val-2017 Task 9: Transition-based language generation from {AMR}. | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2096/ | Lampouras, Gerasimos and Vlachos, Andreas | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 586--591 | This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively. | null | null | 10.18653/v1/S17-2096 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,190 |
inproceedings | ammar-etal-2017-ai2 | The {AI}2 system at {S}em{E}val-2017 Task 10 ({S}cience{IE}): semi-supervised end-to-end entity and relation extraction | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2097/ | Ammar, Waleed and Peters, Matthew E. and Bhagavatula, Chandra and Power, Russell | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 592--596 | This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3). | null | null | 10.18653/v1/S17-2097 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,191 |
inproceedings | baly-etal-2017-omam | {OMAM} at {S}em{E}val-2017 Task 4: Evaluation of {E}nglish State-of-the-Art Sentiment Analysis Models for {A}rabic and a New Topic-based Model | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2099/ | Baly, Ramy and Badaro, Gilbert and Hamdi, Ali and Moukalled, Rawan and Aoun, Rita and El-Khoury, Georges and Al Sallab, Ahmad and Hajj, Hazem and Habash, Nizar and Shaban, Khaled and El-Hajj, Wassim | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 603--610 | While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the {\textquotedblleft}OMAM{\textquotedblright} systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D. | null | null | 10.18653/v1/S17-2099 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,193 |
inproceedings | correa-junior-etal-2017-nilc | {NILC}-{USP} at {S}em{E}val-2017 Task 4: A Multi-view Ensemble for {T}witter Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2100/ | Corr{\^e}a J{\'u}nior, Edilson Anselmo and Marinho, Vanessa Queiroz and dos Santos, Leandro Borges | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 611--615 | This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 systems considering F1 score and 20th considering recall. | null | null | 10.18653/v1/S17-2100 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,194 |
inproceedings | yang-etal-2017-deepsa | deep{SA} at {S}em{E}val-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2101/ | Yang, Tzu-Hsuan and Tseng, Tzu-Hsuan and Chen, Chia-Ping | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 616--620 | In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy. | null | null | 10.18653/v1/S17-2101 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,195 |
inproceedings | yin-etal-2017-nnembs | {NNEMB}s at {S}em{E}val-2017 Task 4: Neural {T}witter Sentiment Classification: a Simple Ensemble Method with Different Embeddings | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2102/ | Yin, Yichun and Song, Yangqiu and Zhang, Ming | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 621--625 | Recently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models. We collect several word embedding sets which are publicly released (often are learned on different corpus) or constructed by running Skip-gram on released large-scale corpus. We make an assumption that different word embeddings cover different words and encode different semantic knowledge, thus using them together can improve the generalizations and performances of neural models. In the SemEval 2017, our method ranks 1st in Accuracy, 5th in AverageR. Meanwhile, the additional comparisons demonstrate the superiority of our model over these ones based on only one word embedding set. We release our code for the method duplicability. | null | null | 10.18653/v1/S17-2102 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,196 |
inproceedings | gupta-yang-2017-crystalnest | {C}rystal{N}est at {S}em{E}val-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2103/ | Gupta, Raj Kumar and Yang, Yinping | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 626--633 | This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets. For sentiment prediction, we developed CrystalNest{--} a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features. We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D. | null | null | 10.18653/v1/S17-2103 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,197 |
inproceedings | jimenez-zafra-etal-2017-sinai | {SINAI} at {S}em{E}val-2017 Task 4: User based classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2104/ | Jim{\'e}nez-Zafra, Salud Mar{\'i}a and Montejo-R{\'a}ez, Arturo and Martin, Maite and Ure{\~n}a-L{\'o}pez, L. Alfonso | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 634--639 | This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user`s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis. | null | null | 10.18653/v1/S17-2104 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,198 |
inproceedings | sarker-gonzalez-2017-hlp | {HLP}@{UP}enn at {S}em{E}val-2017 Task 4{A}: A simple, self-optimizing text classification system combining dense and sparse vectors | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2105/ | Sarker, Abeed and Gonzalez, Graciela | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 640--643 | We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different representations of it are concatenated, and the classification is performed using Support Vector Machines (SVM). Our system is particularly intended for use by non-experts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-score, our system obtained 8th position out of 39 submissions (F1-score: 0.632, average recall: 0.637, accuracy: 0.646). | null | null | 10.18653/v1/S17-2105 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,199 |
inproceedings | dovdon-saias-2017-ej | ej-sa-2017 at {S}em{E}val-2017 Task 4: Experiments for Target oriented Sentiment Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2106/ | Dovdon, Enkhzol and Saias, Jos{\'e} | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 644--647 | This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system. | null | null | 10.18653/v1/S17-2106 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,200 |
inproceedings | troncy-etal-2017-sentime | {S}enti{ME}++ at {S}em{E}val-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2107/ | Troncy, Rapha{\"el and Palumbo, Enrico and Sygkounas, Efstratios and Rizzo, Giuseppe | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 648--652 | In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A {\textquotedblleft}Sentiment Analysis in Twitter{\textquotedblright} that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30{\%} F1-score, ranking 12th out of 38 participants. | null | null | 10.18653/v1/S17-2107 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,201 |
inproceedings | rozental-fleischer-2017-amobee | {A}mobee at {S}em{E}val-2017 Task 4: Deep Learning System for Sentiment Detection on {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2108/ | Rozental, Alon and Fleischer, Daniel | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 653--658 | This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C). | null | null | 10.18653/v1/S17-2108 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,202 |
inproceedings | laskari-sanampudi-2017-twina | {TWINA} at {S}em{E}val-2017 Task 4: {T}witter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2109/ | Laskari, Naveen Kumar and Sanampudi, Suresh Kumar | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 659--663 | This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification {--} Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position. | null | null | 10.18653/v1/S17-2109 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,203 |
inproceedings | mulki-etal-2017-tw | Tw-{S}t{AR} at {S}em{E}val-2017 Task 4: Sentiment Classification of {A}rabic Tweets | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2110/ | Mulki, Hala and Haddad, Hatem and Gridach, Mourad and Babaoglu, Ismail | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 664--669 | In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled {\textquotedblleft}Sentiment analysis in Twitter{\textquotedblright}, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work. | null | null | 10.18653/v1/S17-2110 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,204 |
inproceedings | karpov-2017-nru | {NRU}-{HSE} at {S}em{E}val-2017 Task 4: Tweet Quantification Using Deep Learning Architecture | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2113/ | Karpov, Nikolay | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 683--688 | In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed. | null | null | 10.18653/v1/S17-2113 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,207 |
inproceedings | zhao-etal-2017-mi | {MI}{\&}{T} Lab at {S}em{E}val-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2114/ | Zhao, Jingjing and Yang, Yan and Xu, Bing | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 689--693 | A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence. | null | null | 10.18653/v1/S17-2114 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,208 |
inproceedings | jabreel-moreno-2017-sitaka | {S}i{TAKA} at {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter Based on a Rich Set of Features | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2115/ | Jabreel, Mohammed and Moreno, Antonio | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 694--699 | This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets. | null | null | 10.18653/v1/S17-2115 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,209 |
inproceedings | hamdan-2017-senti17 | {S}enti17 at {S}em{E}val-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2116/ | Hamdan, Hussam | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 700--703 | This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4{\%} average recall. | null | null | 10.18653/v1/S17-2116 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,210 |
inproceedings | s-etal-2017-ssn | {SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter Using Multi-Kernel {G}aussian Process Classifier | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2118/ | S, Angel Deborah and Rajendram, S Milton and Mirnalinee, T T | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 709--712 | The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties. | null | null | 10.18653/v1/S17-2118 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,212 |
inproceedings | wang-etal-2017-ynudlg-semeval | {YNUDLG} at {S}em{E}val-2017 Task 4: A {GRU}-{SVM} Model for Sentiment Classification and Quantification in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2119/ | Wang, Ming and Chu, Biao and Liu, Qingxun and Zhou, Xiaobing | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 713--717 | Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five-tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B and C, respectively. | null | null | 10.18653/v1/S17-2119 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,213 |
inproceedings | htait-etal-2017-lsis | {LSIS} at {S}em{E}val-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For {E}nglish and {A}rabic Tweet Polarity Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2120/ | Htait, Amal and Fournier, S{\'e}bastien and Bellot, Patrice | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 718--722 | We present, in this paper, our contribution in SemEval2017 task 4 : {\textquotedblleft}Sentiment Analysis in Twitter{\textquotedblright}, subtask A: {\textquotedblleft}Message Polarity Classification{\textquotedblright}, for English and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman`s (2003) seed words, on polarity classification of tweet messages. | null | null | 10.18653/v1/S17-2120 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,214 |
inproceedings | gonzalez-etal-2017-elirf | {EL}i{RF}-{UPV} at {S}em{E}val-2017 Task 4: Sentiment Analysis using Deep Learning | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2121/ | Gonz{\'a}lez, Jos{\'e}-{\'A}ngel and Pla, Ferran and Hurtado, Llu{\'i}s-F. | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 723--727 | This paper describes the participation of ELiRF-UPV team at task 4 of SemEval2017. Our approach is based on the use of convolutional and recurrent neural networks and the combination of general and specific word embeddings with polarity lexicons. We participated in all of the proposed subtasks both for English and Arabic languages using the same system with small variations. | null | null | 10.18653/v1/S17-2121 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,215 |
inproceedings | hao-etal-2017-xjsa | {XJSA} at {S}em{E}val-2017 Task 4: A Deep System for Sentiment Classification in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2122/ | Hao, Yazhou and Lan, YangYang and Li, Yufei and Li, Chen | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 728--731 | This paper describes the XJSA System submission from XJTU. Our system was created for SemEval2017 Task 4 {--} subtask A which is very popular and fundamental. The system is based on convolutional neural network and word embedding. We used two pre-trained word vectors and adopt a dynamic strategy for k-max pooling. | null | null | 10.18653/v1/S17-2122 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,216 |
inproceedings | yoon-etal-2017-adullam | Adullam at {S}em{E}val-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2123/ | Yoon, Joosung and Lyu, Kigon and Kim, Hyeoncheol | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 732--736 | We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4). The proposed sentiment analyzer obtained an average F1 of 55.2{\%}, an average recall of 58.9{\%} and an accuracy of 61.4{\%}. | null | null | 10.18653/v1/S17-2123 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,217 |
inproceedings | wang-etal-2017-eica | {EICA} at {S}em{E}val-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2124/ | Wang, Maoquan and Chen, Shiyun and Xie, Yufei and Zhao, Lu | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 737--740 | This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative. | null | null | 10.18653/v1/S17-2124 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,218 |
inproceedings | li-etal-2017-funsentiment | fun{S}entiment at {S}em{E}val-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2125/ | Li, Quanzhi and Nourbakhsh, Armineh and Liu, Xiaomo and Fang, Rui and Shah, Sameena | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 741--746 | This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D {\&} E for English), all of which are about topic-based message polarity classification. Our team is ranked {\#}6 in subtask B, {\#}3 by MAEu and {\#}9 by MAEm in subtask C, {\#}3 using RAE and {\#}6 using KLD in subtask D, and {\#}3 in subtask E. | null | null | 10.18653/v1/S17-2125 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,219 |
inproceedings | baziotis-etal-2017-datastories-semeval | {D}ata{S}tories at {S}em{E}val-2017 Task 4: Deep {LSTM} with Attention for Message-level and Topic-based Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2126/ | Baziotis, Christos and Pelekis, Nikos and Doulkeridis, Christos | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 747--754 | In this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 {\textquotedblleft}Sentiment Analysis in Twitter{\textquotedblright}. We participated in all subtasks for English tweets, involving message-level and topic-based sentiment polarity classification and quantification. We use Long Short-Term Memory (LSTM) networks augmented with two kinds of attention mechanisms, on top of word embeddings pre-trained on a big collection of Twitter messages. Also, we present a text processing tool suitable for social network messages, which performs tokenization, word normalization, segmentation and spell correction. Moreover, our approach uses no hand-crafted features or sentiment lexicons. We ranked 1st (tie) in Subtask A, and achieved very competitive results in the rest of the Subtasks. Both the word embeddings and our text processing tool are available to the research community. | null | null | 10.18653/v1/S17-2126 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,220 |
inproceedings | balikas-2017-twise | {T}wi{S}e at {S}em{E}val-2017 Task 4: Five-point {T}witter Sentiment Classification and Quantification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2127/ | Balikas, Georgios | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 755--759 | The paper describes the participation of the team {\textquotedblleft}TwiSE{\textquotedblright} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {\textquotedblleft}Sentiment Analysis in Twitter{\textquotedblright} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E. | null | null | 10.18653/v1/S17-2127 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,221 |
inproceedings | rouvier-2017-lia | {LIA} at {S}em{E}val-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2128/ | Rouvier, Mickael | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 760--765 | This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6{\%}. | null | null | 10.18653/v1/S17-2128 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,222 |
inproceedings | muller-etal-2017-topicthunder | {T}opic{T}hunder at {S}em{E}val-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2129/ | M{\"uller, Simon and Huonder, Tobias and Deriu, Jan and Cieliebak, Mark | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 766--770 | In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN.With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place. | null | null | 10.18653/v1/S17-2129 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,223 |
inproceedings | miranda-jimenez-etal-2017-ingeotec | {INGEOTEC} at {S}em{E}val 2017 Task 4: A {B}4{MSA} Ensemble based on Genetic Programming for {T}witter Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2130/ | Miranda-Jim{\'e}nez, Sabino and Graff, Mario and Tellez, Eric Sadit and Moctezuma, Daniela | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 771--776 | This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively. | null | null | 10.18653/v1/S17-2130 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,224 |
inproceedings | ayata-etal-2017-busem | {BUSEM} at {S}em{E}val-2017 Task 4{A} Sentiment Analysis with Word Embedding and Long Short Term Memory {RNN} Approaches | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2131/ | Ayata, Deger and Saraclar, Murat and Ozgur, Arzucan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 777--783 | This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algorithms for classification of Twitter messages into negative, neutral and positive polarity. The second system is based on Long Short Term Memory Recurrent Neural Networks and uses word indexes as sequence of inputs for feature representation. | null | null | 10.18653/v1/S17-2131 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,225 |
inproceedings | lozic-etal-2017-takelab | {T}ake{L}ab at {S}em{E}val-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2132/ | Lozi{\'c}, David and {\v{S}}ari{\'c}, Doria and Toki{\'c}, Ivan and Medi{\'c}, Zoran and {\v{S}}najder, Jan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 784--789 | This paper describes the system we submitted to SemEval-2017 Task 4 (Sentiment Analysis in Twitter), specifically subtasks A, B, and D. Our main focus was topic-based message polarity classification on a two-point scale (subtask B). The system we submitted uses a Support Vector Machine classifier with rich set of features, ranging from standard to more creative, task-specific features, including a series of rating-based features as well as features that account for sentimental reminiscence of past topics and deceased famous people. Our system ranked 14th out of 39 submissions in subtask A, 5th out of 24 submissions in subtask B, and 3rd out of 16 submissions in subtask D. | null | null | 10.18653/v1/S17-2132 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,226 |
inproceedings | el-beltagy-etal-2017-niletmrg | {N}ile{TMRG} at {S}em{E}val-2017 Task 4: {A}rabic Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2133/ | El-Beltagy, Samhaa R. and El Kalamawy, Mona and Soliman, Abu Bakr | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 790--795 | This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU`s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one. | null | null | 10.18653/v1/S17-2133 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,227 |
inproceedings | zhang-etal-2017-ynu | {YNU}-{HPCC} at {S}em{E}val 2017 Task 4: Using A Multi-Channel {CNN}-{LSTM} Model for Sentiment Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2134/ | Zhang, Haowei and Wang, Jin and Zhang, Jixian and Zhang, Xuejie | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 796--801 | In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Un-like a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features in different scales. This information is then sequentially composed using LSTM. By combining both CNN and LSTM, we can consider both local information within tweets and long-distance dependency across tweets in the classification process. Officially released results show that our system outperforms the baseline algo-rithm. | null | null | 10.18653/v1/S17-2134 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,228 |
inproceedings | deshmane-friedrichs-2017-tsa | {TSA}-{INF} at {S}em{E}val-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for {T}witter Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2135/ | Deshmane, Amit Ajit and Friedrichs, Jasper | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 802--806 | This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3{\%}. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations. | null | null | 10.18653/v1/S17-2135 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,229 |
inproceedings | abreu-etal-2017-ucsc | {UCSC}-{NLP} at {S}em{E}val-2017 Task 4: Sense n-grams for Sentiment Analysis in {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2136/ | Abreu, Jos{\'e} and Castro, Iv{\'a}n and Mart{\'i}nez, Claudia and Oliva, Sebasti{\'a}n and Guti{\'e}rrez, Yoan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 807--811 | This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an $F_1=0.624$of average. Among 39 submissions to this task, we ranked 10th. | null | null | 10.18653/v1/S17-2136 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,230 |
inproceedings | zhou-etal-2017-ecnu | {ECNU} at {S}em{E}val-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for {T}witter Message Polarity Classification | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2137/ | Zhou, Yunxiao and Lan, Man and Wu, Yuanbin | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 812--816 | This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average. | null | null | 10.18653/v1/S17-2137 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,231 |
inproceedings | mansar-etal-2017-fortia | Fortia-{FBK} at {S}em{E}val-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2138/ | Mansar, Youness and Gatti, Lorenzo and Ferradans, Sira and Guerini, Marco and Staiano, Jacopo | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 817--822 | In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance. | null | null | 10.18653/v1/S17-2138 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,232 |
inproceedings | s-etal-2017-ssn-mlrg1 | {SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel {G}aussian Process Regression Model | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2139/ | S, Angel Deborah and Rajendram, S Milton and Mirnalinee, T T | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 823--826 | The system developed by the SSN{\_}MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously. | null | null | 10.18653/v1/S17-2139 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,233 |
inproceedings | nasim-2017-iba | {IBA}-Sys at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2140/ | Nasim, Zarmeen | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 827--831 | This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github. | null | null | 10.18653/v1/S17-2140 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,234 |
inproceedings | cabanski-etal-2017-hhu | {HHU} at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2141/ | Cabanski, Tobias and Romberg, Julia and Conrad, Stefan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 832--836 | In this Paper a system for solving SemEval-2017 Task 5 is presented. This task is divided into two tracks where the sentiment of microblog messages and news headlines has to be predicted. Since two submissions were allowed, two different machine learning methods were developed to solve this task, a support vector machine approach and a recurrent neural network approach. To feed in data for these approaches, different feature extraction methods are used, mainly word representations and lexica. The best submissions for both tracks are provided by the recurrent neural network which achieves a F1-score of 0.729 in track 1 and 0.702 in track 2. | null | null | 10.18653/v1/S17-2141 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,235 |
inproceedings | zini-etal-2017-inf | {INF}-{UFRGS} at {S}em{E}val-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2142/ | Zini, Tiago and Becker, Karin and Dias, Marcelo | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 837--841 | This paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial Microblogs and News Task. The premise is that it is possible to understand market reaction over a company stock by measuring the positive/negative sentiment contained in the financial tweets and news headlines, where polarity is measured in a continuous scale ranging from -1.0 (very bearish) to 1.0 (very bullish). Our system receives as input the textual content of tweets or news headlines, together with their ids, stock cashtag or name of target company, and the polarity score gold standard for the training dataset. Our solution retrieves features from these text instances using n-gram, hashtags, sentiment score calculated by a external APIs and others features to train a regression model capable to detect continuous score of these sentiments with precision. | null | null | 10.18653/v1/S17-2142 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,236 |
inproceedings | pivovarova-etal-2017-hcs | {HCS} at {S}em{E}val-2017 Task 5: Polarity detection in business news using convolutional neural networks | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2143/ | Pivovarova, Lidia and Escoter, Lloren{\c{c}} and Klami, Arto and Yangarber, Roman | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 842--846 | Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset. | null | null | 10.18653/v1/S17-2143 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,237 |
inproceedings | chen-etal-2017-nlg301 | {NLG}301 at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2144/ | Chen, Chung-Chi and Huang, Hen-Hsen and Chen, Hsin-Hsi | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 847--851 | Short length, multi-targets, target relation-ship, monetary expressions, and outside reference are characteristics of financial tweets. This paper proposes methods to extract target spans from a tweet and its referencing web page. Total 15 publicly available sentiment dictionaries and one sentiment dictionary constructed from training set, containing sentiment scores in binary or real numbers, are used to compute the sentiment scores of text spans. Moreover, the correlation coeffi-cients of the price return between any two stocks are learned with the price data from Bloomberg. They are used to capture the relationships between the interesting tar-get and other stocks mentioned in a tweet. The best result of our method in both sub-task are 56.68{\%} and 55.43{\%}, evaluated by evaluation method 2. | null | null | 10.18653/v1/S17-2144 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,238 |
inproceedings | li-etal-2017-funsentiment-semeval | fun{S}entiment at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from {S}tock{T}wits and {T}witter | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2145/ | Li, Quanzhi and Shah, Sameena and Nourbakhsh, Armineh and Fang, Rui and Liu, Xiaomo | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 852--856 | This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm | null | null | 10.18653/v1/S17-2145 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,239 |
inproceedings | tabari-etal-2017-sentiheros | {S}enti{H}eros at {S}em{E}val-2017 Task 5: An application of Sentiment Analysis on Financial Tweets | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2146/ | Tabari, Narges and Seyeditabari, Armin and Zadrozny, Wlodek | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 857--860 | Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task. | null | null | 10.18653/v1/S17-2146 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,240 |
inproceedings | symeonidis-etal-2017-duth-semeval | {DUTH} at {S}em{E}val-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2147/ | Symeonidis, Symeon and Kordonis, John and Effrosynidis, Dimitrios and Arampatzis, Avi | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 861--865 | We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements {\&} Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model, we used Neural Network Regression, Linear Regression, Boosted Decision Tree Regression and Decision Forrest Regression classifiers to forecast sentiment scores. At the end, we present an error measure, so as to improve the performance about forecasting methods of the system. | null | null | 10.18653/v1/S17-2147 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,241 |
inproceedings | rotim-etal-2017-takelab | {T}ake{L}ab at {S}em{E}val-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2148/ | Rotim, Leon and Tutek, Martin and {\v{S}}najder, Jan | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 866--871 | This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5{--}subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733. | null | null | 10.18653/v1/S17-2148 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,242 |
inproceedings | john-vechtomova-2017-uw | {UW}-{F}in{S}ent at {S}em{E}val-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2149/ | John, Vineet and Vechtomova, Olga | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 872--876 | This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and sentiment score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores. Amongst the methods examined, unigrams and bigrams coupled with simple linear regression obtained the best baseline accuracy. The paper also explores data augmentation methods to supplement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines). | null | null | 10.18653/v1/S17-2149 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,243 |
inproceedings | kar-etal-2017-ritual | {R}i{TUAL}-{UH} at {S}em{E}val-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2150/ | Kar, Sudipta and Maharjan, Suraj and Solorio, Thamar | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 877--882 | In this paper, we present our systems for the {\textquotedblleft}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{\textquotedblright}. In our system, we combined hand-engineered lexical, sentiment and metadata features, the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on top. With this architecture we obtained weighted cosine similarity scores of 0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official scoring system, our system ranked the second place for subtask-2 and eighth place for the subtask-1. It ranked first for both of the subtasks by the scores achieved by an alternate scoring system. | null | null | 10.18653/v1/S17-2150 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,244 |
inproceedings | schouten-etal-2017-commit | {COMMIT} at {S}em{E}val-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2151/ | Schouten, Kim and Frasincar, Flavius and de Jong, Franciska | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 883--887 | This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., {\textquoteleft}decreasing debt' is positive, but {\textquoteleft}decreasing profit' is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position. | null | null | 10.18653/v1/S17-2151 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,245 |
inproceedings | jiang-etal-2017-ecnu | {ECNU} at {S}em{E}val-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2152/ | Jiang, Mengxiao and Lan, Man and Wu, Yuanbin | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 888--893 | This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline domain respectively. To settle this problem, we extract four types of effective features, including linguistic features, sentiment lexicon features, domain-specific features and word embedding features. Then we employ these features to construct models by using ensemble regression algorithms. Our submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively. | null | null | 10.18653/v1/S17-2152 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,246 |
inproceedings | kumar-etal-2017-iitpb | {IITPB} at {S}em{E}val-2017 Task 5: Sentiment Prediction in Financial Text | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2153/ | Kumar, Abhishek and Sethi, Abhishek and Akhtar, Md Shad and Ekbal, Asif and Biemann, Chris and Bhattacharyya, Pushpak | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 894--898 | This paper reports team IITPB`s participation in the SemEval 2017 Task 5 on {\textquoteleft}Fine-grained sentiment analysis on financial microblogs and news'. We developed 2 systems for the two tracks. One system was based on an ensemble of Support Vector Classifier and Logistic Regression. This system relied on Distributional Thesaurus (DT), word embeddings and lexicon features to predict a floating sentiment value between -1 and +1. The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features. The system was ranked 5th in track 1 and 8th in track 2. | null | null | 10.18653/v1/S17-2153 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,247 |
inproceedings | ghosal-etal-2017-iitp | {IITP} at {S}em{E}val-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis | Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David | aug | 2017 | Vancouver, Canada | Association for Computational Linguistics | https://aclanthology.org/S17-2154/ | Ghosal, Deepanway and Bhatnagar, Shobhit and Akhtar, Md Shad and Ekbal, Asif and Bhattacharyya, Pushpak | Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017) | 899--903 | In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts. We build our system to participate in a competition organized by Semantic Evaluation 2017 International Workshop. We combined predictions from various models using an artificial neural network to determine the opinion towards an entity in (a) Microblog Messages and (b) News Headlines data. Our models achieved a cosine similarity score of 0.751 and 0.697 for the above two tracks giving us the rank of 2nd and 7th best team respectively. | null | null | 10.18653/v1/S17-2154 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 56,248 |
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