polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

saleiro-etal-2017-feup

{FEUP} at {S}em{E}val-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word 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/S17-2155/

Saleiro, Pedro and Mendes Rodrigues, Eduarda and Soares, Carlos and Oliveira, Eug{\'e}nio

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

904--908

This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News. The task consisted in predicting a real continuous variable from -1.0 to +1.0 representing the polarity and intensity of sentiment concerning companies/stocks mentioned in short texts. We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial specific bag-of-embeddings. We used an external collection of tweets and news headlines mentioning companies/stocks from S{\&}P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic similarities. The resulting approach obtained a cosine similarity score of 0.69 in sub-task 5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.

null

10.18653/v1/S17-2155

null

56,249

inproceedings

nguyen-nguyen-2017-uit

{UIT}-{DANGNT}-{CLNLP} at {S}em{E}val-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving {CAMR}

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/S17-2156/

Nguyen, Khoa and Nguyen, Dang

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

909--913

This paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to increase the performance of CAMR when parsing sentences from scientific articles, especially articles of biology domain more accurately. To achieve this goal, we built two wrapper layers for CAMR. The first layer, which covers the input data, will normalize, add necessary information to the input sentences to make the input dependency parser and the aligner better handle reference citations, scientific figures, formulas, etc. The second layer, which covers the output data, will modify and standardize output data based on a list of scientific concept fixing patterns. This will help CAMR better handle biological concepts which are not in the training dataset. Finally, after applying our approach, CAMR has scored 0.65 F-score on the test set of Biomedical training data and 0.61 F-score on the official blind test dataset.

null

10.18653/v1/S17-2156

null

56,250

inproceedings

buys-blunsom-2017-oxford

{O}xford at {S}em{E}val-2017 Task 9: Neural {AMR} Parsing with Pointer-Augmented 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/S17-2157/

Buys, Jan and Blunsom, Phil

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

914--919

We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59{\%} Smatch on the SemEval test set.

null

10.18653/v1/S17-2157

null

56,251

inproceedings

gruzitis-etal-2017-rigotrio

{RIGOTRIO} at {S}em{E}val-2017 Task 9: Combining Machine Learning and Grammar Engineering for {AMR} 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/S17-2159/

Gruzitis, Normunds and Gosko, Didzis and Barzdins, Guntis

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

924--928

By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3{\%} of the test AMRs, therefore we combined it with state-of-the-art JAMR Generator to see if the combination increases or decreases the overall performance. The combined system achieved the automatic BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to the plain JAMR Generator results. As for AMR parsing, we added NER extensions to our SemEval-2016 general-domain AMR parser to handle the biomedical genre, rich in organic compound names, achieving Smatch F1=54.0{\%}.

null

10.18653/v1/S17-2159

null

56,253

inproceedings

van-noord-bos-2017-meaning

The Meaning Factory at {S}em{E}val-2017 Task 9: Producing {AMR}s with Neural Semantic Parsing

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/S17-2160/

van Noord, Rik and Bos, Johan

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

929--933

We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.

null

10.18653/v1/S17-2160

null

56,254

inproceedings

wang-li-2017-pku

{PKU}{\_}{ICL} at {S}em{E}val-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge

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/S17-2161/

Wang, Liang and Li, Sujian

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

934--937

This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed approach utilizes external knowledge to enrich feature representation of candidate keyphrase, including Wikipedia, IEEE taxonomy and pre-trained word embeddings etc. Ensemble of unsupervised models, random forest and linear models are used for candidate keyphrase ranking and keyphrase type classification. Our system achieves the 3rd place in subtask A and 4th place in subtask B.

null

10.18653/v1/S17-2161

null

56,255

inproceedings

marsi-etal-2017-ntnu

{NTNU}-1@{S}cience{IE} at {S}em{E}val-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields

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/S17-2162/

Marsi, Erwin and Sikdar, Utpal Kumar and Marco, Cristina and Barik, Biswanath and S{\ae}tre, Rune

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

938--941

We present NTNU`s systems for Task A (prediction of keyphrases) and Task B (labelling as Material, Process or Task) at SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our approach relies on supervised machine learning using Conditional Random Fields. Our system yields a micro F-score of 0.34 for Tasks A and B combined on the test data. For Task C (relation extraction), we relied on an independently developed system described in (Barik and Marsi, 2017). For the full Scenario 1 (including relations), our approach reaches a micro F-score of 0.33 (5th place). Here we describe our systems, report results and discuss errors.

null

10.18653/v1/S17-2162

null

56,256

inproceedings

eger-etal-2017-eelection

{EELECTION} at {S}em{E}val-2017 Task 10: Ensemble of n{E}ural Learners for k{E}yphrase {C}lassifica{TION}

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/S17-2163/

Eger, Steffen and Do Dinh, Erik-L{\^a}n and Kuznetsov, Ilia and Kiaeeha, Masoud and Gurevych, Iryna

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

942--946

This paper describes our approach to the SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications, specifically to Subtask (B): Classification of identified keyphrases. We explored three different deep learning approaches: a character-level convolutional neural network (CNN), a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM. From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-$F_1$-score of 0.63 on the test data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four according to this official score. However, we erroneously trained 2 out of 3 neural nets (the stacker and the CNN) on only roughly 15{\%} of the full data, namely, the original development set. When trained on the full data (training+development), our ensemble has a micro-$F_{1}$-score of 0.69. Our code is available from \url{https://github.com/UKPLab/semeval2017-scienceie}.

null

10.18653/v1/S17-2163

null

56,257

inproceedings

segura-bedmar-etal-2017-labda

{LABDA} at {S}em{E}val-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the {BANNER} tool and the {UMLS} Semantic Network

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/S17-2164/

Segura-Bedmar, Isabel and Col{\'o}n-Ruiz, Crist{\'o}bal and Mart{\'i}nez, Paloma

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

947--950

This paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For the task of identification, we use the BANNER tool, a named entity recognition system, which is based on conditional random fields (CRF) and has obtained successful results in the biomedical domain. To classify keyphrases, we study the UMLS semantic network and propose a possible linking between the keyphrase types and the UMLS semantic groups. Based on this semantic linking, we create a dictionary for each keyphrase type. Then, a feature indicating if a token is found in one of these dictionaries is incorporated to feature set used by the BANNER tool. The final results on the test dataset show that our system still needs to be improved, but the conditional random fields and, consequently, the BANNER system can be used as a first approximation to identify and classify keyphrases.

null

10.18653/v1/S17-2164

null

56,258

inproceedings

lee-etal-2017-ntnu

The {NTNU} System at {S}em{E}val-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields

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/S17-2165/

Lee, Lung-Hao and Lee, Kuei-Ching and Tseng, Yuen-Hsien

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

951--955

This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify keyphrases along with categorized labels and their relations from scientific publications. A total of 16 teams participated in evaluation scenario 1 (subtasks A, B, and C), with only 7 teams competing in all sub-tasks. Our best micro-averaging F1 across the three subtasks is 0.23, ranking in the middle among all 16 submissions.

null

10.18653/v1/S17-2165

null

56,259

inproceedings

liu-etal-2017-mayonlp

{M}ayo{NLP} at {S}em{E}val 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in 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/S17-2166/

Liu, Sijia and Shen, Feichen and Chaudhary, Vipin and Liu, Hongfang

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

956--960

In this paper, we present MayoNLP`s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection.

null

10.18653/v1/S17-2166

null

56,260

inproceedings

kern-etal-2017-know

Know-Center at {S}em{E}val-2017 Task 10: Sequence Classification with the {CODE} Annotator

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/S17-2167/

Kern, Roman and Falk, Stefan and Rexha, Andi

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

961--964

This paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them with one of the three categories: Task, Process, and Material. These scientific publications are selected from Computer Science, Material Sciences, and Physics domains. We followed a supervised approach for both subtasks by using a sequential classifier (CRF - Conditional Random Fields). For generating our solution we used a web-based application implemented in the EU-funded research project, named CODE. Our system achieved an F1 score of 0.39 for the Subtask 1 and 0.28 for the Subtask 2.

null

10.18653/v1/S17-2167

null

56,261

inproceedings

barik-marsi-2017-ntnu

{NTNU}-2 at {S}em{E}val-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents

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/S17-2168/

Barik, Biswanath and Marsi, Erwin

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

965--968

This paper presents our relation extraction system for subtask C of SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already annotated in the input data, our work explores a wide range of linguistic features, applies various feature selection techniques, optimizes the hyper parameters and class weights and experiments with different problem formulations (single classification model vs individual classifiers for each keyphrase type, single-step classifier vs pipeline classifier for hyponym relations). Performance of five popular classification algorithms are evaluated for each problem formulation along with feature selection. The best setting achieved an F1 score of 71.0{\%} for synonym and 30.0{\%} for hyponym relation on the test data.

null

10.18653/v1/S17-2168

null

56,262

inproceedings

suarez-paniagua-etal-2017-labda

{LABDA} at {S}em{E}val-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

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/S17-2169/

Su{\'a}rez-Paniagua, V{\'i}ctor and Segura-Bedmar, Isabel and Mart{\'i}nez, Paloma

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

969--972

In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38{\%} for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.

null

10.18653/v1/S17-2169

null

56,263

inproceedings

prasad-kan-2017-wing

{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling

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/S17-2170/

Prasad, Animesh and Kan, Min-Yen

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

973--977

We describe an end-to-end pipeline processing approach for SemEval 2017`s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.

null

10.18653/v1/S17-2170

null

56,264

inproceedings

lee-etal-2017-mit

{MIT} at {S}em{E}val-2017 Task 10: Relation Extraction with 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/S17-2171/

Lee, Ji Young and Dernoncourt, Franck and Szolovits, Peter

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

978--984

Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts. Artificial neural networks have recently been explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations. Our model ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).

null

10.18653/v1/S17-2171

null

56,265

inproceedings

tsujimura-etal-2017-tti

{TTI}-{COIN} at {S}em{E}val-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers

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/S17-2172/

Tsujimura, Tomoki and Miwa, Makoto and Sasaki, Yutaka

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

985--989

This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. We participated in the full task setting of the entity segmentation, entity classification and relation classification (scenario 1) and the setting of relation classification only (scenario 3). The system was directly applied to the scenario 1 without modifying the codes thanks to its generality and flexibility. Our evaluation results show that the choice of appropriate pre-trained embeddings affected the performance significantly. With the best embeddings, our system was ranked third in the scenario 1 with the micro F1 score of 0.38. We also confirm that our system can produce the micro F1 score of 0.48 for the scenario 3 on the test data, and this score is close to the score of the 3rd ranked system in the task.

null

10.18653/v1/S17-2172

null

56,266

inproceedings

berend-2017-szte

{SZTE}-{NLP} at {S}em{E}val-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature 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/S17-2173/

Berend, G{\'a}bor

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

990--994

In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents. We aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible. Without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia, our proposed system manages to perform among the best participating systems in terms of precision.

null

10.18653/v1/S17-2173

null

56,267

inproceedings

hernandez-etal-2017-lipn

{LIPN} at {S}em{E}val-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling 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/S17-2174/

Hernandez, Simon David and Buscaldi, Davide and Charnois, Thierry

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

995--999

This paper describes the system used by the team LIPN in SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.

null

10.18653/v1/S17-2174

null

56,268

inproceedings

kubis-etal-2017-eudamu

{EUDAMU} at {S}em{E}val-2017 Task 11: Action Ranking and Type Matching for End-User Development

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/S17-2175/

Kubis, Marek and Sk{\'o}rzewski, Pawe{\l} and Zi{\k{e}}tkiewicz, Tomasz

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1000--1004

The paper describes a system for end-user development using natural language. Our approach uses a ranking model to identify the actions to be executed followed by reference and parameter matching models to select parameter values that should be set for the given commands. We discuss the results of evaluation and possible improvements for future work.

null

10.18653/v1/S17-2175

null

56,269

inproceedings

p-r-etal-2017-hitachi

Hitachi at {S}em{E}val-2017 Task 12: System for temporal information extraction from clinical notes

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/S17-2176/

P R, Sarath and R, Manikandan and Niwa, Yoshiki

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1005--1009

This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of the 2017 Clinical TempEval challenge. Clinical TempEval 2017 addressed the problem of temporal reasoning in the clinical domain by providing annotated clinical notes, pathology and radiology reports in line with Clinical TempEval challenges 2015/16, across two different evaluation phases focusing on cross domain adaptation. Our team focused on subtasks involving extractions of temporal spans and relations for which the developed systems showed average F-score of 0.45 and 0.47 across the two phases of evaluations.

null

10.18653/v1/S17-2176

null

56,270

inproceedings

huang-etal-2017-ntu

{NTU}-1 at {S}em{E}val-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation

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/S17-2177/

Huang, Po-Yu and Huang, Hen-Hsen and Wang, Yu-Wun and Huang, Ching and Chen, Hsin-Hsi

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1010--1013

This study proposes a system to participate in the Clinical TempEval 2017 shared task, a part of the SemEval 2017 Tasks. Domain adaptation was the main challenge this year. We took part in the supervised domain adaption where data of 591 records of colon cancer patients and 30 records of brain cancer patients from Mayo clinic were given and we are asked to analyze the records from brain cancer patients. Based on the THYME corpus released by the organizer of Clinical TempEval, we propose a framework that automatically analyzes clinical temporal events in a fine-grained level. Support vector machine (SVM) and conditional random field (CRF) were implemented in our system for different subtasks, including detecting clinical relevant events and time expression, determining their attributes, and identifying their relations with each other within the document. The results showed the capability of domain adaptation of our system.

null

10.18653/v1/S17-2177

null

56,271

inproceedings

long-etal-2017-xjnlp

{XJNLP} at {S}em{E}val-2017 Task 12: Clinical temporal information ex-traction with a Hybrid 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/S17-2178/

Long, Yu and Li, Zhijing and Wang, Xuan and Li, Chen

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1014--1018

Temporality is crucial in understanding the course of clinical events from a patient`s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.

null

10.18653/v1/S17-2178

null

56,272

inproceedings

lamurias-etal-2017-ulisboa

{ULISBOA} at {S}em{E}val-2017 Task 12: Extraction and classification of temporal expressions and events

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/S17-2179/

Lamurias, Andre and Sousa, Diana and Pereira, Sofia and Clarke, Luka and Couto, Francisco M.

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1019--1023

This paper presents our approach to participate in the SemEval 2017 Task 12: Clinical TempEval challenge, specifically in the event and time expressions span and attribute identification subtasks (ES, EA, TS, TA). Our approach consisted in training Conditional Random Fields (CRF) classifiers using the provided annotations, and in creating manually curated rules to classify the attributes of each event and time expression. We used a set of common features for the event and time CRF classifiers, and a set of features specific to each type of entity, based on domain knowledge. Training only on the source domain data, our best F-scores were 0.683 and 0.485 for event and time span identification subtasks. When adding target domain annotations to the training data, the best F-scores obtained were 0.729 and 0.554, for the same subtasks. We obtained the second highest F-score of the challenge on the event polarity subtask (0.708). The source code of our system, Clinical Timeline Annotation (CiTA), is available at \url{https://github.com/lasigeBioTM/CiTA}.

null

10.18653/v1/S17-2179

null

56,273

inproceedings

macavaney-etal-2017-guir

{GUIR} at {S}em{E}val-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information 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/S17-2180/

MacAvaney, Sean and Cohan, Arman and Goharian, Nazli

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1024--1029

Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches include conditional random fields and decision tree ensembles, using lexical, syntactic, semantic, distributional, and rule-based features. Our system received best or second best scores in TIMEX3 span, EVENT span, and CONTAINS relation extraction.

null

10.18653/v1/S17-2180

null

56,274

inproceedings

leeuwenberg-moens-2017-kuleuven

{KUL}euven-{LIIR} at {S}em{E}val-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records

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/S17-2181/

Leeuwenberg, Artuur and Moens, Marie-Francine

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1030--1034

In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and analyze the results from our submissions, and verify the effectiveness of several system components. Our system performed above average for all subtasks in both phases.

null

10.18653/v1/S17-2181

null

56,275

inproceedings

abualhaija-etal-2017-parameter

Parameter Transfer across Domains for Word Sense Disambiguation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1001/

Abualhaija, Sallam and Tahmasebi, Nina and Forin, Diane and Zimmermann, Karl-Heinz

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

1--8

Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words in a given context with a maximum semantic similarity. Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the word sense disambiguation problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing, hence, it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.

null

10.26615/978-954-452-049-6_001

null

56,277

inproceedings

aburaed-etal-2017-sentence

What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1002/

AbuRa{'}ed, Ahmed and Chiruzzo, Luis and Saggion, Horacio

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

9--17

In the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece of scientific work, however they are limited in that they do not provide information about what specific part of the reference paper the citing paper is referring to. This qualitative information is very important, for example, in the context of current community-based scientific summarization activities. In this paper, and relying on an annotated dataset of co-citation sentences, we carry out a number of experiments aimed at, given a citation sentence, automatically identify a part of a reference paper being cited. Additionally our algorithm predicts the specific reason why such reference sentence has been cited out of five possible reasons.

null

10.26615/978-954-452-049-6_002

null

56,278

inproceedings

agirrezabal-etal-2017-comparison

A Comparison of Feature-Based and Neural Scansion of Poetry

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1003/

Agirrezabal, Manex and Alegria, I{\~n}aki and Hulden, Mans

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

18--23

Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.

null

10.26615/978-954-452-049-6_003

null

56,279

inproceedings

ahmadnia-etal-2017-persian

{P}ersian-{S}panish Low-Resource Statistical Machine Translation Through {E}nglish as Pivot Language

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1004/

Ahmadnia, Benyamin and Serrano, Javier and Haffari, Gholamreza

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

24--30

This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian-English SMT is combined with the English-Spanish one, where the relatively large corpora of each may be used in support of the Persian-Spanish pairing. Our results indicate that the pivot language technique outperforms the direct SMT processes currently in use between Persian and Spanish. Furthermore, we investigate the sentence translation pivot strategy and the phrase translation in turn, and demonstrate that, in the context of the Persian-Spanish SMT system, the phrase-level pivoting outperforms the sentence-level pivoting. Finally we suggest a method called combination model in which the standard direct model and the best triangulation pivoting model are blended in order to reach a high-quality translation.

null

10.26615/978-954-452-049-6_004

null

56,280

inproceedings

aker-etal-2017-simple

Simple Open Stance Classification for Rumour Analysis

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1005/

Aker, Ahmet and Derczynski, Leon and Bontcheva, Kalina

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

31--39

Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.

null

10.26615/978-954-452-049-6_005

null

56,281

inproceedings

aker-etal-2017-extensible

An Extensible Multilingual Open Source Lemmatizer

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1006/

Aker, Ahmet and Petrak, Johann and Sabbah, Firas

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

40--45

We present GATE DictLemmatizer, a multilingual open source lemmatizer for the GATE NLP framework that currently supports English, German, Italian, French, Dutch, and Spanish, and is easily extensible to other languages. The software is freely available under the LGPL license. The lemmatization is based on the Helsinki Finite-State Transducer Technology (HFST) and lemma dictionaries automatically created from Wiktionary. We evaluate the performance of the lemmatizers against TreeTagger, which is only freely available for research purposes. Our evaluation shows that DictLemmatizer achieves similar or even better results than TreeTagger for languages where there is support from HFST. The performance drops when there is no support from HFST and the entire lemmatization process is based on lemma dictionaries. However, the results are still satisfactory given the fact that DictLemmatizer isopen-source and can be easily extended to other languages. The software for extending the lemmatizer by creating word lists from Wiktionary dictionaries is also freely available as open-source software.

null

10.26615/978-954-452-049-6_006

null

56,282

inproceedings

albogamy-ramsay-2017-universal

{U}niversal {D}ependencies for {A}rabic Tweets

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1007/

Albogamy, Fahad and Ramsay, Allan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

46--51

To facilitate cross-lingual studies, there is an increasing interest in identifying linguistic universals. Recently, a new universal scheme was designed as a part of universal dependency project. In this paper, we map the Arabic tweets dependency treebank (ATDT) to the Universal Dependency (UD) scheme to compare it to other language resources and for the purpose of cross-lingual studies.

null

10.26615/978-954-452-049-6_007

null

56,283

inproceedings

almansor-al-ani-2017-translating

Translating Dialectal {A}rabic as Low Resource Language using Word Embedding

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1008/

Almansor, Ebtesam H and Al-Ani, Ahmed

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

52--57

A number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and their dialects. These methods have produced promising results when applied to some of the widely studied languages. Existing translation methods are mainly implemented using rule-based and static machine translation approaches. Rule based approaches utilize language translation rules that can either be constructed by an expert, which is quite difficult when dealing with dialects, or rely on rule construction algorithms, which require very large parallel datasets. Statistical approaches also require large parallel datasets to build the translation models. However, large parallel datasets do not exist for languages with low resources, such as the Arabic language and its dialects. In this paper we propose an algorithm that attempts to overcome this limitation, and apply it to translate the Egyptian dialect (EGY) to Modern Standard Arabic (MSA). Monolingual corpus was collected for both MSA and EGY and a relatively small parallel language pair set was built to train the models. The proposed method utilizes Word embedding as it requires monolingual data rather than parallel corpus. Both Continuous Bag of Words and Skip-gram were used to build word vectors. The proposed method was validated on four different datasets using a four-fold cross validation approach.

null

10.26615/978-954-452-049-6_008

null

56,284

inproceedings

almiman-ramsay-2017-using

Using {E}nglish Dictionaries to generate Commonsense Knowledge in Natural Language

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1009/

Almiman, Ali and Ramsay, Allan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

58--63

This paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side {\ensuremath{\Rightarrow}} right-hand side. In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions. A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are {\textquoteleft}True' or not. For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.

null

10.26615/978-954-452-049-6_009

null

56,285

inproceedings

almiman-ramsay-2017-hybrid

A Hybrid System to apply Natural Language Inference over Dependency Trees

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1010/

Almiman, Ali and Ramsay, Allan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

64--70

This paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The theorem prover is customized to accept dependency trees and apply inference rules to these trees. The inference rules are automatically generated as syllogistic rules from our test data (FraCaS test suite). The theorem prover exploits a non-deterministic matching algorithm within a standard backward chaining inference engine. We employ continuation programming as a way of seamlessly handling the combination of these two non-deterministic algorithms. Testing the matching algorithm on {\textquotedblleft}Generalized quantifiers{\textquotedblright} and {\textquotedblleft}adjectives{\textquotedblright} topics in FraCaS (MacCartney and Manning 2007), we achieved an accuracy of 92.8{\%} of the single-premise cases. For the multi-steps of inference, we checked the validity of our syllogistic rules and then extracted four generic instances that can be applied to more than one problem.

null

10.26615/978-954-452-049-6_010

null

56,286

inproceedings

barbu-2017-ensembles

Ensembles of Classifiers for Cleaning Web Parallel Corpora and Translation Memories

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1011/

Barbu, Eduard

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

71--77

The last years witnessed an increasing interest in the automatic methods for spotting false translation units in translation memories. This problem presents a great interest to industry as there are many translation memories that contain errors. A closely related line of research deals with identifying sentences that do not align in the parallel corpora mined from the web. The task of spotting false translations is modeled as a binary classification problem. It is known that in certain conditions the ensembles of classifiers improve over the performance of the individual members. In this paper we benchmark the most popular ensemble of classifiers: Majority Voting, Bagging, Stacking and Ada Boost at the task of spotting false translation units for translation memories and parallel web corpora. We want to know if for this specific problem any ensemble technique improves the performance of the individual classifiers and if there is a difference between the data in translation memories and parallel web corpora with respect to this task.

null

10.26615/978-954-452-049-6_011

null

56,287

inproceedings

basaldella-etal-2017-exploiting

Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1012/

Basaldella, Marco and Helmy, Muhammad and Antolli, Elisa and Popescu, Mihai Horia and Serra, Giuseppe and Tasso, Carlo

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

78--85

This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.

null

10.26615/978-954-452-049-6_012

null

56,288

inproceedings

bastawisy-elmahdy-2017-multi

Multi-Lingual Phrase-Based Statistical Machine Translation for {A}rabic-{E}nglish

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1013/

Bastawisy, Ahmed and Elmahdy, Mohamed

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

86--89

In this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic differ significantly. Different mono-lingual and multi-lingual hybrid SMT approaches are compared. Mono-lingual systems do always results in better translation accuracy in one Arabic form and poor accuracy in the other. Multi-lingual SMT models that are trained with pooled parallel MSA/dialectal data result in better accuracy. However, since the available parallel MSA data are much larger compared to dialectal data, multilingual models are biased to MSA. We propose in the work, a multi-lingual combination of different mono-lingual systems using an Arabic form classifier. The outcome of the classier directs the system to use the appropriate mono-lingual models (standard, dialectal, or mixture). Testing the different SMT systems shows that the proposed classifier-based SMT system outperforms mono-lingual and data pooled multi-lingual systems.

null

10.26615/978-954-452-049-6_013

null

56,289

inproceedings

benikova-zesch-2017-different

Same same, but different: Compositionality of paraphrase granularity levels

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1014/

Benikova, Darina and Zesch, Torsten

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

90--96

Paraphrases exist on different granularity levels, the most frequently used one being the sentential level. However, we argue that working on the sentential level is not optimal for both machines and humans, and that it would be easier and more efficient to work on sub-sentential levels. To prove this, we quantify and analyze the difference between paraphrases on both sentence and sub-sentence level in order to show the significance of the problem. First results on a preliminary dataset seem to confirm our hypotheses.

null

10.26615/978-954-452-049-6_014

null

56,290

inproceedings

bobicev-sokolova-2017-inter

Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1015/

Bobicev, Victoria and Sokolova, Marina

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

97--102

Manual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results. Reliability of annotations and adequacy of assigned labels are especially important in the case of sentiment annotations. In the current study we examine inter-annotator agreement in multi-class, multi-label sentiment annotation of messages. We used several annotation agreement measures, as well as statistical analysis and Machine Learning to assess the resulting annotations.

null

10.26615/978-954-452-049-6_015

null

56,291

inproceedings

boros-etal-2017-fast

Fast and Accurate Decision Trees for Natural Language Processing Tasks

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1016/

Boros, Tiberiu and Dumitrescu, Stefan Daniel and Pipa, Sonia

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

103--110

Decision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development set to delete nodes from over-fitted models. The later mentioned algorithm also uses a results caching method for speed-up. Our algorithm is almost 200 times faster than a naive implementation and yields accurate results on our test datasets.

null

10.26615/978-954-452-049-6_016

null

56,292

inproceedings

bossard-rodrigues-2017-evolutionary

An Evolutionary Algorithm for Automatic Summarization

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1017/

Bossard, Aur{\'e}lien and Rodrigues, Christophe

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

111--120

This paper proposes a novel method to select sentences for automatic summarization based on an evolutionary algorithm. The algorithm explores candidate summaries space following an objective function computed over ngrams probability distributions of the candidate summary and the source documents. This method does not consider a summary as a stack of independent sentences but as a whole text, and makes use of advances in unsupervised summarization evaluation. We compare this sentence extraction method to one of the best existing methods which is based on integer linear programming, and show its efficiency on three different acknowledged corpora.

null

10.26615/978-954-452-049-6_017

null

56,293

inproceedings

boyanov-etal-2017-building

Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1018/

Boyanov, Martin and Nakov, Preslav and Moschitti, Alessandro and Da San Martino, Giovanni and Koychev, Ivan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

121--129

We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5{\%} on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5{\%} of the questions when they are similar in style to how questions are asked in the forum, and 47.3{\%} of the questions, when they are more conversational in style.

null

10.26615/978-954-452-049-6_018

null

56,294

inproceedings

boytcheva-etal-2017-mining

Mining Association Rules from Clinical Narratives

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1019/

Boytcheva, Svetla and Nikolova, Ivelina and Angelova, Galia

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

130--138

Shallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining approaches are used mainly for structured information in databases and are a quite challenging task in text mining. Unfortunately, most frequent pattern mining approaches do not use contextual information for extracted patterns: general patterns are extracted regardless of the context. We propose a method that processes raw informal texts (from health discussion forums) and formal texts (outpatient records) in Bulgarian language. In addition we use some context information and small terminological lexicons to generalize extracted frequent patterns. This allows to map informal expression of medical terminology to the formal one and to generate automatically resources.

null

10.26615/978-954-452-049-6_019

null

56,295

inproceedings

calixto-liu-2017-sentence

Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1020/

Calixto, Iacer and Liu, Qun

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

139--148

We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of three or more input sources. We compare our model against different baselines, and evaluate the robustness of our embeddings on image{--}sentence ranking (ISR), semantic textual similarity (STS), and neural machine translation (NMT). We find that the additional multilingual signals lead to improvements on all three tasks, and we highlight that our model can be used to consistently improve the adequacy of translations generated with NMT models when re-ranking n-best lists.

null

10.26615/978-954-452-049-6_020

null

56,296

inproceedings

calleja-etal-2017-role

Role-based model for Named Entity Recognition

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1021/

Calleja, Pablo and Garc{\'i}a-Castro, Ra{\'u}l and Aguado-de-Cea, Guadalupe and G{\'o}mez-P{\'e}rez, Asunci{\'o}n

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

149--156

Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.

null

10.26615/978-954-452-049-6_021

null

56,297

inproceedings

canales-etal-2017-towards

Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1022/

Canales, Lea and Daelemans, Walter and Boldrini, Ester and Mart{\'i}nez-Barco, Patricio

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

157--163

Emotion detection has a high potential positive impact on the benefit of business, society, politics or education. Given this, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection: emotional corpora annotation. This will be tackled by proposing a semi-automatic methodology. It consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a manual process of refinement where human annotators will determine which is the dominant emotion between the pre-defined set. Our objective in this paper is to show the pre-annotation process, as well as to evaluate the usability of subjective and polarity information in this process. The evaluation performed confirms clearly the benefits of employing the polarity and subjective information on emotion detection and thus endorses the relevance of our approach.

null

10.26615/978-954-452-049-6_022

null

56,298

inproceedings

chen-bangalore-2017-underspecification

Underspecification in Natural Language Understanding for Dialog Automation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1023/

Chen, John and Bangalore, Srinivas

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

164--170

With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems. These systems, typically known as \textit{chatbots} have been successfully applied in a range of consumer and enterprise applications. A key technology in such chat-bots is robust natural language understanding (NLU) which can significantly influence and impact the efficacy of the conversation and ultimately the user-experience. While NLU is far from perfect, this paper illustrates the role of \textit{underspecification} and its impact on successful dialog completion.

null

10.26615/978-954-452-049-6_023

null

56,299

inproceedings

chiru-decea-2017-identification

Identification and Classification of the Most Important Moments in Students' Collaborative Chats

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1024/

Chiru, Costin and Decea, Remus

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

171--176

In this paper, we present an application for the automatic identification of the important moments that might occur during students' collaborative chats. The moments are detected based on the input received from the user, who may choose to perform an analysis on the topics that interest him/her. Moreover, the application offers various types of suggestive and intuitive graphics that aid the user in identification of such moments. There are two main aspects that are considered when identifying important moments: the concepts' frequency and distribution throughout the conversation and the chat tempo, which is analyzed for identifying intensively debated concepts. By the tempo of the chat we understand the rate at which the ideas are input by the chat participants, expressed by the utterances' timestamps.

null

10.26615/978-954-452-049-6_024

null

56,300

inproceedings

cotik-etal-2017-annotation

Annotation of Entities and Relations in {S}panish Radiology Reports

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1025/

Cotik, Viviana and Filippo, Dar{\'i}o and Roller, Roland and Uszkoreit, Hans and Xu, Feiyu

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

177--184

Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.

null

10.26615/978-954-452-049-6_025

null

56,301

inproceedings

dakota-kubler-2017-towards

Towards Replicability in Parsing

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1026/

Dakota, Daniel and K{\"ubler, Sandra

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

185--194

We investigate parsing replicability across 7 languages (and 8 treebanks), showing that choices concerning the use of grammatical functions in parsing or evaluation, the influence of the rare word threshold, as well as choices in test sentences and evaluation script options have considerable and often unexpected effects on parsing accuracies. All of those choices need to be carefully documented if we want to ensure replicability.

null

10.26615/978-954-452-049-6_026

null

56,302

inproceedings

davoodi-kosseim-2017-automatic

Automatic Identification of {A}lt{L}exes using Monolingual Parallel Corpora

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1027/

Davoodi, Elnaz and Kosseim, Leila

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

195--200

The automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as since or but, are the most informative cues to identify explicit relations; however discourse parsers typically use a closed inventory of such connectives. As a result, discourse relations signalled by markers outside these inventories (i.e. AltLexes) are not detected as effectively. In this paper, we propose a novel method to leverage parallel corpora in text simplification and lexical resources to automatically identify alternative lexicalizations that signal discourse relation. When applied to the Simple Wikipedia and Newsela corpora along with WordNet and the PPDB, the method allowed the automatic discovery of 91 AltLexes.

null

10.26615/978-954-452-049-6_027

null

56,303

inproceedings

dinu-etal-2017-stylistic

On the stylistic evolution from communism to democracy: {S}olomon {M}arcus study case

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1028/

Dinu, Anca and Dinu, Liviu P. and Dumitru, Bogdan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

201--207

In this article we propose a stylistic analysis of Solomon Marcus' non-scientific published texts, gathered in six volumes, aiming to uncover some of his quantitative and qualitative fingerprints. Moreover, we compare and cluster two distinct periods of time in his writing style: 22 years of communist regime (1967-1989) and 27 years of democracy (1990-2016). The distributional analysis of Marcus' text reveals that the passing from the communist regime period to democracy is sharply marked by two complementary changes in Marcus' writing: in the pre-democracy period, the communist norms of writing style demanded on the one hand long phrases, long words and clich{\'e}s, and on the other hand, a short list of preferred {\textquotedblleft}official{\textquotedblright} topics; in democracy tendency was towards shorten phrases and words while approaching a broader area of topics.

null

10.26615/978-954-452-049-6_028

null

56,304

inproceedings

edouard-etal-2017-building

Building timelines of soccer matches from {T}witter

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1029/

Edouard, Amosse and Cabrio, Elena and Tonelli, Sara and Le-Thanh, Nhan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

208--213

This demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) and participants of a game (e.g. players, teams). In the demo, a web application displays in nearly real-time the actions detected from tweets posted by users for a given match of Euro 2016. Our tools are freely available at \url{https://bitbucket.org/eamosse/event_tracking}.

null

10.26615/978-954-452-049-6_029

null

56,305

inproceedings

edouard-etal-2017-youll

You`ll Never Tweet Alone: Building Sports Match Timelines from Microblog Posts

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1030/

Edouard, Amosse and Cabrio, Elena and Tonelli, Sara and Le-Thanh, Nhan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

214--221

In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets collected during the EURO 2016 Championship and evaluate the output against live summaries produced by sports channels.

null

10.26615/978-954-452-049-6_030

null

56,306

inproceedings

edouard-etal-2017-graph

Graph-based Event Extraction from {T}witter

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1031/

Edouard, Amosse and Cabrio, Elena and Tonelli, Sara and Le-Thanh, Nhan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

222--230

Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events.

null

10.26615/978-954-452-049-6_031

null

56,307

inproceedings

fernandez-etal-2017-opinion

Opinion Mining in Social Networks versus Electoral Polls

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1032/

Fern{\'a}ndez, Javi and Llopis, Fernando and Guti{\'e}rrez, Yoan and Mart{\'i}nez-Barco, Patricio and D{\'i}ez, {\'A}lvaro

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

231--237

The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential elections with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.

null

10.26615/978-954-452-049-6_032

null

56,308

inproceedings

galarreta-etal-2017-corpus

Corpus Creation and Initial {SMT} Experiments between {S}panish and {S}hipibo-konibo

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1033/

Galarreta, Ana-Paula and Melgar, Andr{\'e}s and Oncevay, Arturo

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

238--244

In this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp). There are very few digital texts written in Shipibo-konibo and even less bilingual texts that can be aligned, hence we had to create a parallel corpus using both bilingual and monolingual texts. We will describe how this corpus was made, as well as the process we followed to improve the quality of the sentences used to build a statistical MT model or SMT. The results obtained surpassed the baseline proposed (dictionary based) and made a promising result for further development considering the size of corpus used. Finally, it is expected that this MT system can be reinforced with the use of additional linguistic rules and automatic language processing functions that are being implemented.

null

10.26615/978-954-452-049-6_033

null

56,309

inproceedings

galieva-etal-2017-russian

{R}ussian-{T}atar Socio-Political Thesaurus: Methodology, Challenges, the Status of the Project

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1034/

Galieva, Alfiya and Nevzorova, Olga and Yakubova, Dilyara

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

245--252

This paper discusses the general methodology and important practical aspects of implementing a new bilingual lexical resource {--} the Russian-Tatar Socio-Political Thesaurus that is being developed on the basis of the Russian RuThes thesaurus format as a hierarchy of concepts viewed as units of thought. Each concept is linked with a set of language expressions (words and collocations) referring to it in texts (text entries). Currently the Russian-Tatar Socio-Political Thesaurus includes 6,000 concepts, while new concepts and text entries are being constantly added to it. The paper outlines main challenges of translating concept names and their text entries into Tatar, and describes ways of reflecting the specificity of the Tatar lexical-semantic system.

null

10.26615/978-954-452-049-6_034

null

56,310

inproceedings

galitsky-ilvovsky-2017-chat

On a Chat Bot Finding Answers with Optimal Rhetoric Representation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1035/

Galitsky, Boris and Ilvovsky, Dmitry

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

253--259

We demo a chat bot with the focus on complex, multi-sentence questions that enforce what we call rhetoric agreement of answers with questions. Chat bot finds answers which are not only relevant by topic but also match the question by style, argumentation patterns, communication means, experience level and other attributes. The system achieves rhetoric agreement by learning pairs of discourse trees (DTs) for question (Q) and answer (A). We build a library of best answer DTs for most types of complex questions. To better recognize a valid rhetoric agreement between Q and A, DTs are extended with the labels for communicative actions. An algorithm for finding the best DT for an A, given a Q, is evaluated.

null

10.26615/978-954-452-049-6_035

null

56,311

inproceedings

gao-huang-2017-detecting

Detecting Online Hate Speech Using Context Aware Models

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1036/

Gao, Lei and Huang, Ruihong

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

260--266

In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3{\%} to 4{\%} in F1 score and combining these two models further improve the performance by another 7{\%} in F1 score.

null

10.26615/978-954-452-049-6_036

null

56,312

inproceedings

gencheva-etal-2017-context

A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1037/

Gencheva, Pepa and Nakov, Preslav and M{\`a}rquez, Llu{\'i}s and Barr{\'o}n-Cede{\~n}o, Alberto and Koychev, Ivan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

267--276

In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.

null

10.26615/978-954-452-049-6_037

null

56,313

inproceedings

gromann-declerck-2017-hashtag

Hashtag Processing for Enhanced Clustering of Tweets

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1038/

Gromann, Dagmar and Declerck, Thierry

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

277--283

Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.

null

10.26615/978-954-452-049-6_038

null

56,314

inproceedings

guillen-etal-2017-natural

Natural Language Processing Technologies for Document Profiling

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1039/

Guill{\'e}n, Antonio and Guti{\'e}rrez, Yoan and Mu{\~n}oz, Rafael

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

284--290

Nowadays, search for documents on the Internet is becoming increasingly difficult. The reason is the amount of content published by users (articles, comments, blogs, reviews). How to facilitate that the users can find their required documents? What would be necessary to provide useful document meta-data for supporting search engines? In this article, we present a study of some Natural Language Processing (NLP) technologies that can be useful for facilitating the proper identification of documents according to the user needs. For this purpose, it is designed a document profile that will be able to represent semantic meta-data extracted from documents by using NLP technologies. The research is basically focused on the study of different NLP technologies in order to support the creation our novel document profile proposal from semantic perspectives.

null

10.26615/978-954-452-049-6_039

null

56,315

inproceedings

hazem-etal-2017-mappsent

{M}app{S}ent: a Textual Mapping Approach for Question-to-Question Similarity

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1040/

Hazem, Amir and El Amel Boussaha, Basma and Hernandez, Nicolas

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

291--300

Since the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. Mikolov et al. (2013) have demonstrated that words and phrases exhibit linear structures that allow to meaningfully combine words by an element-wise addition of their vector representations. Recently, Arora et al. (2017) have shown that removing the projections of the weighted average sum of word embedding vectors on their first principal components, outperforms sophisticated supervised methods including RNN`s and LSTM`s. Inspired by Mikolov et al. (2013) and Arora et al. (2017) findings and by a bilingual word mapping technique presented in Artetxe et al. (2016), we introduce MappSent, a novel approach for textual similarity. Based on a linear sentence embedding representation, its principle is to build a matrix that maps sentences in a joint-subspace where similar sets of sentences are pushed closer. We evaluate our approach on the SemEval 2016/2017 question-to-question similarity task and show that overall MappSent achieves competitive results and outperforms in most cases state-of-art methods.

null

10.26615/978-954-452-049-6_040

null

56,316

inproceedings

hercig-lenc-2017-impact

The Impact of Figurative Language on Sentiment Analysis

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1041/

Hercig, Tom{\'a}{\v{s}} and Lenc, Ladislav

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

301--308

Figurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits of sentiment analysis of supposedly literal texts. We explore the effect of figurative language on sentiment analysis. We incorporate the figurative language indicators into the sentiment analysis process and compare the results with and without the additional information about them. We evaluate on the SemEval-2015 Task 11 data and outperform the first team with our convolutional neural network model and additional training data in terms of mean squared error and we follow closely behind the first place in terms of cosine similarity.

null

10.26615/978-954-452-049-6_041

null

56,317

inproceedings

hooda-kosseim-2017-argument

Argument Labeling of Explicit Discourse Relations using {LSTM} Neural Networks

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1042/

Hooda, Sohail and Kosseim, Leila

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

309--315

Argument labeling of explicit discourse relations is a challenging task. The state of the art systems achieve slightly above 55{\%} F-measure but require hand-crafted features. In this paper, we propose a Long Short Term Memory (LSTM) based model for argument labeling. We experimented with multiple configurations of our model. Using the PDTB dataset, our best model achieved an F1 measure of 23.05{\%} without any feature engineering. This is significantly higher than the 20.52{\%} achieved by the state of the art RNN approach, but significantly lower than the feature based state of the art systems. On the other hand, because our approach learns only from the raw dataset, it is more widely applicable to multiple textual genres and languages.

null

10.26615/978-954-452-049-6_042

null

56,318

inproceedings

hu-etal-2017-non

Non-Deterministic Segmentation for {C}hinese Lattice Parsing

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1043/

Hu, Hai and Dakota, Daniel and K{\"ubler, Sandra

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

316--324

Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.

null

10.26615/978-954-452-049-6_043

null

56,319

inproceedings

kanishcheva-bobicev-2017-good

Good News vs. Bad News: What are they talking about?

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1044/

Kanishcheva, Olga and Bobicev, Victoria

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

325--333

Today`s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people`s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.

null

10.26615/978-954-452-049-6_044

null

56,320

inproceedings

karadzhov-etal-2017-built

We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1045/

Karadzhov, Georgi and Gencheva, Pepa and Nakov, Preslav and Koychev, Ivan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

334--343

It is completely amazing! Fake news and {\textquotedblleft}click baits{\textquotedblright} have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason {\#}2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research, and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and clickbait in the Bulgarian cyberspace. You won`t believe what we have found!

null

10.26615/978-954-452-049-6_045

null

56,321

inproceedings

karadzhov-etal-2017-fully

Fully Automated Fact Checking Using External Sources

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1046/

Karadzhov, Georgi and Nakov, Preslav and M{\`a}rquez, Llu{\'i}s and Barr{\'o}n-Cede{\~n}o, Alberto and Koychev, Ivan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

344--353

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.

null

10.26615/978-954-452-049-6_046

null

56,322

inproceedings

kaushik-etal-2017-making

Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1047/

Kaushik, Divyansh and Gupta, Shashank and Raju, Chakradhar and Dias, Reuben and Ghosh, Sanjib

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

354--362

The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and e-tickets are usually machine generated by filling slots in pre-defined templates which improve the presentation of such emails but also make the emails more complex in structure. Extracting the relevant information from these emails would let users track their journeys and important updates on applications installed on their devices to give them a consolidated over view of their itineraries and also save valuable time. We investigate the use of an HMM-based named entity recognizer on such emails which we will use to label and extract relevant entities. NER in such emails is challenging as these itineraries offer less useful contextual information. We also propose a rich set of features which are integrated into the model and are specific to our domain. The result from our model is a list of lists containing the relevant information extracted from ones itinerary.

null

10.26615/978-954-452-049-6_047

null

56,323

inproceedings

kedzia-etal-2017-graph

Graph-Based Approach to Recognizing {CST} Relations in {P}olish Texts

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1048/

K{\k{e}}dzia, Pawe{\l} and Piasecki, Maciej and Janz, Arkadiusz

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

363--371

This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.

null

10.26615/978-954-452-049-6_048

null

56,324

inproceedings

kobus-etal-2017-domain

Domain Control for Neural Machine Translation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1049/

Kobus, Catherine and Crego, Josep and Senellart, Jean

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

372--378

Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have already been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.

null

10.26615/978-954-452-049-6_049

null

56,325

inproceedings

kocmi-bojar-2017-curriculum

Curriculum Learning and Minibatch Bucketing in Neural Machine Translation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1050/

Kocmi, Tom and Bojar, Ond{\v{r}}ej

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

379--386

We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called {\textquotedblleft}curriculum learning{\textquotedblright}). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our {\textquotedblleft}curricula{\textquotedblright} achieve a small improvement over the baseline.

null

10.26615/978-954-452-049-6_050

null

56,326

inproceedings

kocon-marcinczuk-2017-improved

Improved Recognition and Normalisation of {P}olish Temporal Expressions

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1051/

Koco{\'n}, Jan and Marci{\'n}czuk, Micha{\l}

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

387--393

In this article we present the result of the recent research in the recognition and normalisation of Polish temporal expressions. The temporal information extracted from the text plays major role in many information extraction systems, like question answering, event recognition or discourse analysis. We proposed a new method for the temporal expressions normalisation, called Cascade of Partial Rules. Here we describe results achieved by updated version of Liner2 machine learning system.

null

10.26615/978-954-452-049-6_051

null

56,327

inproceedings

konkol-2017-joint

Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1052/

Konkol, Michal

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

394--400

In this paper, we introduce WoRel, a model that jointly learns word embeddings and a semantic representation of word relations. The model learns from plain text sentences and their dependency parse trees. The word embeddings produced by WoRel outperform Skip-Gram and GloVe in word similarity and syntactical word analogy tasks and have comparable results on word relatedness and semantic word analogy tasks. We show that the semantic representation of relations enables us to express the meaning of phrases and is a promising research direction for semantics at the sentence level.

null

10.26615/978-954-452-049-6_052

null

56,328

inproceedings

konopik-etal-2017-czech

{C}zech Dataset for Semantic Similarity and Relatedness

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1053/

Konop{\'i}k, Miloslav and Pra{\v{z}}{\'a}k, Ond{\v{r}}ej and Steinberger, David

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

401--406

This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is $r = 0.81$. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.

null

10.26615/978-954-452-049-6_053

null

56,329

inproceedings

laali-kosseim-2017-improving

Improving Discourse Relation Projection to Build Discourse Annotated Corpora

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1054/

Laali, Majid and Kosseim, Leila

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

407--416

The naive approach to annotation projection is not effective to project discourse annotations from one language to another because implicit relations are often changed to explicit ones and vice-versa in the translation. In this paper, we propose a novel approach based on the intersection between statistical word-alignment models to identify unsupported discourse annotations. This approach identified 65{\%} of the unsupported annotations in the English-French parallel sentences from Europarl. By filtering out these unsupported annotations, we induced the first PDTB-style discourse annotated corpus for French from Europarl. We then used this corpus to train a classifier to identify the discourse-usage of French discourse connectives and show a 15{\%} improvement of F1-score compared to the classifier trained on the non-filtered annotations.

null

10.26615/978-954-452-049-6_054

null

56,330

inproceedings

lafourcade-etal-2017-mice

If mice were reptiles, then reptiles could be mammals or How to detect errors in the {J}eux{D}e{M}ots lexical network?

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1056/

Lafourcade, Mathieu and Joubert, Alain and Le Brun, Nathalie

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

424--430

Correcting errors in a data set is a critical issue. This task can be either hand-made by experts, or by crowdsourcing methods, or automatically done using algorithms. Although the rate of errors present in the JeuxDeMots network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect {\textquotedblleft}erroneous{\textquotedblright} initial relations, which could lead to the automatic detection of the majority of errors in the network.

null

10.26615/978-954-452-049-6_056

null

56,332

inproceedings

lenc-kral-2017-word

Word Embeddings for Multi-label Document Classification

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1057/

Lenc, Ladislav and Kr{\'a}l, Pavel

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

431--437

In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech {\v{C}}TK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.

null

10.26615/978-954-452-049-6_057

null

56,333

inproceedings

li-dickinson-2017-gender

Gender Prediction for {C}hinese Social Media Data

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1058/

Li, Wen and Dickinson, Markus

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

438--445

Social media provides users a platform to publish messages and socialize with others, and microblogs have gained more users than ever in recent years. With such usage, user profiling is a popular task in computational linguistics and text mining. Different approaches have been used to predict users' gender, age, and other information, but most of this work has been done on English and other Western languages. The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform. Given issues in Chinese word segmentation, we explore character and word n-grams as features for this task, as well as using character and word embeddings for classification. Given how the data is extracted, we approach the task on a per-post basis, and we show the difficulties of the task for both humans and computers. Nonetheless, we present encouraging results and point to future improvements.

null

10.26615/978-954-452-049-6_058

null

56,334

inproceedings

liao-xie-2017-statistical

A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1059/

Liao, Zhihua and Xie, Yan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

446--451

In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.

null

10.26615/978-954-452-049-6_059

null

56,335

inproceedings

londhe-srihari-2017-summarizing

Summarizing World Speak : A Preliminary Graph Based Approach

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1060/

Londhe, Nikhil and Srihari, Rohini

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

452--458

Social media platforms play a crucial role in piecing together global news stories via their corresponding online discussions. Thus, in this work, we introduce the problem of automatically summarizing massively multilingual microblog text streams. We discuss the challenges involved in both generating summaries as well as evaluating them. We introduce a simple word graph based approach that utilizes node neighborhoods to identify keyphrases and thus in turn, pick summary candidates. We also demonstrate the effectiveness of our method in generating precise summaries as compared to other popular techniques.

null

10.26615/978-954-452-049-6_060

null

56,336

inproceedings

loukachevitch-gerasimova-2017-human

Human Associations Help to Detect Conventionalized Multiword Expressions

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1061/

Loukachevitch, Natalia and Gerasimova, Anastasia

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

459--466

In this paper we show that if we want to obtain human evidence about conventionalization of some phrases, we should ask native speakers about associations they have to a given phrase and its component words. We have shown that if component words of a phrase have each other as frequent associations, then this phrase can be considered as conventionalized. Another type of conventionalized phrases can be revealed using two factors: low entropy of phrase associations and low intersection of component word and phrase associations. The association experiments were performed for the Russian language.

null

10.26615/978-954-452-049-6_061

null

56,337

inproceedings

malmasi-zampieri-2017-detecting

Detecting Hate Speech in Social Media

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1062/

Malmasi, Shervin and Zampieri, Marcos

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

467--472

In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78{\%} accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.

null

10.26615/978-954-452-049-6_062

null

56,338

inproceedings

marcinczuk-etal-2017-inforex

{I}nforex {---} a collaborative system for text corpora annotation and analysis

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1063/

Marci{\'n}czuk, Micha{\l} and Oleksy, Marcin and Koco{\'n}, Jan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

473--482

We report a first major upgrade of Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. Inforex is a part of Polish CLARIN infrastructure. It is integrated with a digital repository for storing and publishing language resources and allows to visualize, browse and annotate text corpora stored in the repository. As a result of a series of workshops for researches from humanities and social sciences fields we improved the graphical interface to make the system more friendly and readable for non-experienced users. We also implemented a new functionality for gold standard annotation which includes private annotations and annotation agreement by a super-annotator.

null

10.26615/978-954-452-049-6_063

null

56,339

inproceedings

marcinczuk-2017-lemmatization

Lemmatization of Multi-word Common Noun Phrases and Named Entities in {P}olish

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1064/

Marci{\'n}czuk, Micha{\l}

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

483--491

In the paper we present a tool for lemmatization of multi-word common noun phrases and named entities for Polish called LemmaPL. The tool is based on a set of manually crafted rules and heuristics utilizing a set of dictionaries (including morphological, named entities and inflection patterns). The accuracy of lemmatization obtained by the tool reached 97.99{\%} on a dataset with multi-word common noun phrases and 86.17{\%} for case-sensitive evaluation on a dataset with named entities.

null

10.26615/978-954-452-049-6_064

null

56,340

inproceedings

mi-etal-2017-log

Log-linear Models for {U}yghur Segmentation in Spoken Language Translation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1065/

Mi, Chenggang and Yang, Yating and Dong, Rui and Zhou, Xi and Wang, Lei and Li, Xiao and Jiang, Tonghai

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

492--500

To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.

null

10.26615/978-954-452-049-6_065

null

56,341

inproceedings

mitrofan-2017-bootstrapping

Bootstrapping a {R}omanian Corpus for Medical Named Entity Recognition

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1066/

Mitrofan, Maria

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

501--509

Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, it was created a sub-corpus specific to this domain. In this paper we present a newly developed Romanian sub-corpus for medical-domain NER, which is a valuable asset for the field of biomedical text processing. We provide a description of the sub-corpus, informative statistics about data-composition and we evaluate an automatic NER tool on the newly created resource.

null

10.26615/978-954-452-049-6_066

null

56,342

inproceedings

moreno-etal-2017-domain

A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1067/

Moreno, Isabel and Rom{\'a}-Ferri, Mar{\'i}a Teresa and Moreda Pozo, Paloma

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

510--518

This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.

null

10.26615/978-954-452-049-6_067

null

56,343

inproceedings

mukherjee-kubler-2017-similarity

Similarity Based Genre Identification for {POS} Tagging Experts {\&} Dependency Parsing

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1068/

Mukherjee, Atreyee and K{\"ubler, Sandra

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

519--526

POS tagging and dependency parsing achieve good results for homogeneous datasets. However, these tasks are much more difficult on heterogeneous datasets. In (Mukherjee et al. 2016, 2017), we address this issue by creating genre experts for both POS tagging and parsing. We use topic modeling to automatically separate training and test data into genres and to create annotation experts per genre by training separate models for each topic. However, this approach assumes that topic modeling is performed jointly on training and test sentences each time a new test sentence is encountered. We extend this work by assigning new test sentences to their genre expert by using similarity metrics. We investigate three different types of methods: 1) based on words highly associated with a genre by the topic modeler, 2) using a k-nearest neighbor classification approach, and 3) using perplexity to determine the closest topic. The results show that the choice of similarity metric has an effect on results and that we can reach comparable accuracies to the joint topic modeling in POS tagging and dependency parsing, thus providing a viable and efficient approach to POS tagging and parsing a sentence by its genre expert.

null

10.26615/978-954-452-049-6_068

null

56,344

inproceedings

naderi-hirst-2017-recognizing

Recognizing Reputation Defence Strategies in Critical Political Exchanges

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1069/

Naderi, Nona and Hirst, Graeme

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

527--535

We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and reputation defence, we determine the reputation defence strategy. We annotate a dataset of parliamentary questions and answers with reputation defence strategies. We then propose a model based on supervised learning to address the detection of these strategies, and report promising experimental results.

null

10.26615/978-954-452-049-6_069

null

56,345

inproceedings

naderi-hirst-2017-classifying

Classifying Frames at the Sentence Level in News Articles

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1070/

Naderi, Nona and Hirst, Graeme

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

536--542

Previous approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames at the sentence level in news articles. We conduct our experiments on the publicly available Media Frames Corpus compiled from the U.S. Newspapers. Using (B)LSTMs and GRU networks to represent the meaning of frames, we demonstrate that our approach yields at least 14-point improvement over several baseline methods.

null

10.26615/978-954-452-049-6_070

null

56,346

inproceedings

nakov-vogel-2017-robust

Robust Tuning Datasets for Statistical Machine Translation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1071/

Nakov, Preslav and Vogel, Stephan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

543--550

We explore the idea of automatically crafting a tuning dataset for Statistical Machine Translation (SMT) that makes the hyper-parameters of the SMT system more robust with respect to some specific deficiencies of the parameter tuning algorithms. This is an under-explored research direction, which can allow better parameter tuning. In this paper, we achieve this goal by selecting a subset of the available sentence pairs, which are more suitable for specific combinations of optimizers, objective functions, and evaluation measures. We demonstrate the potential of the idea with the pairwise ranking optimization (PRO) optimizer, which is known to yield too short translations. We show that the learning problem can be alleviated by tuning on a subset of the development set, selected based on sentence length. In particular, using the longest 50{\%} of the tuning sentences, we achieve two-fold tuning speedup, and improvements in BLEU score that rival those of alternatives, which fix BLEU+1`s smoothing instead.

null

10.26615/978-954-452-049-6_071

null

56,347

inproceedings

nakov-etal-2017-trust

Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1072/

Nakov, Preslav and Mihaylova, Tsvetomila and M{\`a}rquez, Llu{\'i}s and Shiroya, Yashkumar and Koychev, Ivan

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

551--560

We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted. First, we motivate the problem and we create a publicly available annotated English corpus by crowdsourcing. Second, we propose a large set of features to predict the credibility of the answers. The features model the user, the answer, the question, the thread as a whole, and the interaction between them. Our experiments with ranking SVMs show that the credibility labels can be predicted with high performance according to several standard IR ranking metrics, thus supporting the potential usage of this layer of credibility information in practical applications. The features modeling the profile of the user (in particular trollness) turn out to be most important, but embedding features modeling the answer and the similarity between the question and the answer are also very relevant. Overall, half of the gap between the baseline performance and the perfect classifier can be covered using the proposed features.

null

10.26615/978-954-452-049-6_072

null

56,348

inproceedings

osenova-simov-2017-bulgarian

{B}ulgarian-{E}nglish and {E}nglish-{B}ulgarian Machine Translation: System Design and Evaluation

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1073/

Osenova, Petya and Simov, Kiril

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

561--568

The paper presents a deep factored machine translation (MT) system between English and Bulgarian languages in both directions. The MT system is hybrid. It consists of three main steps: (1) the source-language text is linguistically annotated, (2) it is translated to the target language with the Moses system, and (3) translation is post-processed with the help of the transferred linguistic annotation from the source text. Besides automatic evaluation we performed manual evaluation over a domain test suite of sentences demonstrating certain phenomena like imperatives, questions, etc.

null

10.26615/978-954-452-049-6_073

null

56,349

inproceedings

paul-das-2017-identification

Identification of Character Adjectives from {M}ahabharata

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1074/

Paul, Apurba and Das, Dipankar

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

569--576

The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the tra-ditional approaches of named entity identifica-tion, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the pres-ence of characters in different text spans. Such six patterns were used in order to extract the cha-racters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.

null

10.26615/978-954-452-049-6_074

null

56,350

inproceedings

perez-estruch-etal-2017-learning

Learning Multimodal Gender Profile using Neural Networks

Mitkov, Ruslan and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/R17-1075/

P{\'e}rez Estruch, Carlos and Paredes Palacios, Roberto and Rosso, Paolo

Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017

577--582

Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8{\%}) obtaining the state-of-the-art performance of 91.3{\%}.

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10.26615/978-954-452-049-6_075

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56,351