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

inproceedings

hsu-etal-2017-hybrid

A Hybrid {CNN}-{RNN} Alignment Model for Phrase-Aware Sentence Classification

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2071/

Hsu, Shiou Tian and Moon, Changsung and Jones, Paul and Samatova, Nagiza

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

443--449

The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structure-independent {\textquoteleft}Gated Representation Alignment' (GRA) model that blends a phrase-focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6{\%}) of fine-grained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.

null

57,389

inproceedings

nikolentzos-etal-2017-multivariate

Multivariate {G}aussian Document Representation from Word Embeddings for Text Categorization

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2072/

Nikolentzos, Giannis and Meladianos, Polykarpos and Rousseau, Fran{\c{c}}ois and Stavrakas, Yannis and Vazirgiannis, Michalis

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

450--455

Recently, there has been a lot of activity in learning distributed representations of words in vector spaces. Although there are models capable of learning high-quality distributed representations of words, how to generate vector representations of the same quality for phrases or documents still remains a challenge. In this paper, we propose to model each document as a multivariate Gaussian distribution based on the distributed representations of its words. We then measure the similarity between two documents based on the similarity of their distributions. Experiments on eight standard text categorization datasets demonstrate the effectiveness of the proposed approach in comparison with state-of-the-art methods.

null

57,390

inproceedings

li-mak-2017-derivation

Derivation of Document Vectors from Adaptation of {LSTM} Language Model

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2073/

Li, Wei and Mak, Brian

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

456--461

In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document. This paper proposes a novel distributed vector representation of a document, which will be labeled as DV-LSTM, and is derived from the result of adapting a long short-term memory recurrent neural network language model by the document. DV-LSTM is expected to capture some high-level sequential information in the document, which other current document representations fail to do. It was evaluated in document genre classification in the Brown Corpus and the BNC Baby Corpus. The results show that DV-LSTM significantly outperforms TF-IDF vector and paragraph vector (PV-DM) in most cases, and their combinations may further improve the classification performance.

null

57,391

inproceedings

meladianos-etal-2017-real

Real-Time Keyword Extraction from Conversations

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2074/

Meladianos, Polykarpos and Tixier, Antoine and Nikolentzos, Ioannis and Vazirgiannis, Michalis

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

462--467

We introduce a novel method to extract keywords from meeting speech in real-time. Our approach builds on the graph-of-words representation of text and leverages the k-core decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.

null

57,392

inproceedings

eric-manning-2017-copy

A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2075/

Eric, Mihail and Manning, Christopher

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

468--473

Task-oriented dialogue focuses on conversational agents that participate in dialogues with user goals on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7{\%} in per-response generation and is on par with the current state-of-the-art on DSTC2, a real-world task-oriented dialogue dataset.

null

57,393

inproceedings

bansal-etal-2017-towards

Towards speech-to-text translation without speech recognition

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2076/

Bansal, Sameer and Kamper, Herman and Lopez, Adam and Goldwater, Sharon

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

474--479

We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.

null

57,394

inproceedings

keizer-etal-2017-evaluating

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2077/

Keizer, Simon and Guhe, Markus and Cuay{\'a}huitl, Heriberto and Efstathiou, Ioannis and Engelbrecht, Klaus-Peter and Dobre, Mihai and Lascarides, Alex and Lemon, Oliver

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

480--484

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {\textquotedblleft}Settlers of Catan{\textquotedblright}. The comparison is based on human subjects playing games against artificial game-playing agents ({\textquoteleft}bots') which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

null

57,395

inproceedings

brychcin-kral-2017-unsupervised

Unsupervised Dialogue Act Induction using {G}aussian Mixtures

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2078/

Brychc{\'i}n, Tom{\'a}{\v{s}} and Kr{\'a}l, Pavel

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

485--490

This paper introduces a new unsupervised approach for dialogue act induction. Given the sequence of dialogue utterances, the task is to assign them the labels representing their function in the dialogue. Utterances are represented as real-valued vectors encoding their meaning. We model the dialogue as Hidden Markov model with emission probabilities estimated by Gaussian mixtures. We use Gibbs sampling for posterior inference. We present the results on the standard Switchboard-DAMSL corpus. Our algorithm achieves promising results compared with strong supervised baselines and outperforms other unsupervised algorithms.

null

57,396

inproceedings

han-schlangen-2017-grounding

Grounding Language by Continuous Observation of Instruction Following

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2079/

Han, Ting and Schlangen, David

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

491--496

Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.

null

57,397

inproceedings

van-der-klis-etal-2017-mapping

Mapping the Perfect via Translation Mining

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2080/

van der Klis, Martijn and Le Bruyn, Bert and de Swart, Henri{\"ette

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

497--502

Semantic analyses of the Perfect often defeat their own purpose: by restricting their attention to {\textquoteleftreal' perfects (like the English one), they implicitly assume the Perfect has predefined meanings and usages. We turn the tables and focus on form, using data extracted from multilingual parallel corpora to automatically generate semantic maps (Haspelmath, 1997) of the sequence {\textquoteleftHave/Be + past participle' in five European languages (German, English, Spanish, French, Dutch). This technique, which we dub Translation Mining, has been applied before in the lexical domain (W{\"alchli and Cysouw, 2012) but we showcase its application at the level of the grammar.

null

57,398

inproceedings

poliak-etal-2017-efficient

Efficient, Compositional, Order-sensitive n-gram Embeddings

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2081/

Poliak, Adam and Rastogi, Pushpendre and Martin, M. Patrick and Van Durme, Benjamin

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

503--508

We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.

null

57,399

inproceedings

wang-ma-2017-integrating

Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2082/

Wang, Hsin-Yang and Ma, Wei-Yun

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

509--515

Distributional word representations are widely used in NLP tasks. These representations are based on an assumption that words with a similar context tend to have a similar meaning. To improve the quality of the context-based embeddings, many researches have explored how to make full use of existing lexical resources. In this paper, we argue that while we incorporate the prior knowledge with context-based embeddings, words with different occurrences should be treated differently. Therefore, we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings - different words would have different learning rates when adjusting their embeddings. In the result, we demonstrate that our embeddings get significant improvements on two different tasks: Word Similarity and Analogical Reasoning.

null

57,400

inproceedings

klein-etal-2017-improving

Improving Neural Knowledge Base Completion with Cross-Lingual Projections

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2083/

Klein, Patrick and Ponzetto, Simone Paolo and Glava{\v{s}}, Goran

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

516--522

In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.

null

57,401

inproceedings

bulat-etal-2017-modelling

Modelling metaphor with attribute-based semantics

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2084/

Bulat, Luana and Clark, Stephen and Shutova, Ekaterina

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

523--528

One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.

null

57,402

inproceedings

weeds-etal-2017-red

When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2085/

Weeds, Julie and Kober, Thomas and Reffin, Jeremy and Weir, David

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

529--534

Non-compositional phrases such as \textit{red herring} and weakly compositional phrases such as \textit{spelling bee} are an integral part of natural language (Sag, 2002). They are also the phrases that are difficult, or even impossible, for good compositional distributional models of semantics. Compositionality detection therefore provides a good testbed for compositional methods. We compare an integrated compositional distributional approach, using sparse high dimensional representations, with the ad-hoc compositional approach of applying simple composition operations to state-of-the-art neural embeddings.

null

57,403

inproceedings

koper-schulte-im-walde-2017-applying

Applying Multi-Sense Embeddings for {G}erman Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2086/

K{\"oper, Maximilian and Schulte im Walde, Sabine

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

535--542

Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level. In this paper, we compare and extend multi-sense embeddings, in order to model and utilise word senses on the token level. We focus on the challenging class of complex verbs, and evaluate the model variants on various semantic tasks: semantic classification; predicting compositionality; and detecting non-literal language usage. While there is no overall best model, all models significantly outperform a word2vec single-sense skip baseline, thus demonstrating the need to distinguish between word senses in a distributional semantic model.

null

57,404

inproceedings

ustalov-etal-2017-negative

Negative Sampling Improves Hypernymy Extraction Based on Projection Learning

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2087/

Ustalov, Dmitry and Arefyev, Nikolay and Biemann, Chris and Panchenko, Alexander

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

543--550

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.

null

57,405

inproceedings

sobhani-etal-2017-dataset

A Dataset for Multi-Target Stance Detection

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2088/

Sobhani, Parinaz and Inkpen, Diana and Zhu, Xiaodan

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

551--557

Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification.

null

57,406

inproceedings

gimenez-perez-etal-2017-single

Single and Cross-domain Polarity Classification using String Kernels

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2089/

Gim{\'e}nez-P{\'e}rez, Rosa M. and Franco-Salvador, Marc and Rosso, Paolo

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

558--563

The polarity classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain polarity classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain polarity classification.

null

57,407

inproceedings

sedoc-etal-2017-predicting

Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2090/

Sedoc, Jo{\~a}o and Preo{\c{t}}iuc-Pietro, Daniel and Ungar, Lyle

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

564--571

Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word`s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.

null

57,408

inproceedings

liu-zhang-2017-attention

Attention Modeling for Targeted Sentiment

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2091/

Liu, Jiangming and Zhang, Yue

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

572--577

Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.

null

57,409

inproceedings

buechel-hahn-2017-emobank

{E}mo{B}ank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2092/

Buechel, Sven and Hahn, Udo

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

578--585

We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer`s and reader`s emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader`s perspective in terms of IAA and rating intensity, and achieve close-to-human performance when mapping between dimensional and categorical formats.

null

57,410

inproceedings

kokkinos-potamianos-2017-structural

Structural Attention Neural Networks for improved sentiment analysis

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2093/

Kokkinos, Filippos and Potamianos, Alexandros

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

586--591

We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree.

null

57,411

inproceedings

adel-etal-2017-ranking

Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced {T}witter Data

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2094/

Adel, Heike and Chen, Francine and Chen, Yan-Ying

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

592--598

Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define the task of classifying the purchase stage of each tweet in a user`s tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.

null

57,412

inproceedings

li-etal-2017-context

Context-Aware Graph Segmentation for Graph-Based Translation

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2095/

Li, Liangyou and Way, Andy and Liu, Qun

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

599--604

In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese{--}English and German{--}English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.

null

57,413

inproceedings

jakubina-langlais-2017-reranking

Reranking Translation Candidates Produced by Several Bilingual Word Similarity Sources

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2096/

Jakubina, Laurent and Langlais, Phillippe

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

605--611

We investigate the reranking of the output of several distributional approaches on the Bilingual Lexicon Induction task. We show that reranking an n-best list produced by any of those approaches leads to very substantial improvements. We further demonstrate that combining several n-best lists by reranking is an effective way of further boosting performance.

null

57,414

inproceedings

siahbani-sarkar-2017-lexicalized

Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2097/

Siahbani, Maryam and Sarkar, Anoop

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

612--618

Phrase-based and hierarchical phrase-based (Hiero) translation models differ radically in the way reordering is modeled. Lexicalized reordering models play an important role in phrase-based MT and such models have been added to CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary order and produces the translation in left to right (LR) order which leads to far fewer language model calls and leads to a considerable speedup in decoding. We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our lexicalized reordering model (LRM) and show that it improves translation quality for Czech-English, Chinese-English and German-English.

null

57,415

inproceedings

hauer-etal-2017-bootstrapping

Bootstrapping Unsupervised Bilingual Lexicon Induction

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2098/

Hauer, Bradley and Nicolai, Garrett and Kondrak, Grzegorz

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

619--624

The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on the basis of their frequency and lexical similarity. We apply bidirectional bootstrapping to a method which learns a linear mapping between context-based vector spaces. Experimental results on three language pairs show consistent improvement over prior work.

null

57,416

inproceedings

weller-di-marco-etal-2017-addressing

Addressing Problems across Linguistic Levels in {SMT}: Combining Approaches to Model Morphology, Syntax and Lexical Choice

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2099/

Weller-Di Marco, Marion and Fraser, Alexander and Schulte im Walde, Sabine

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

625--630

Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice. We explore combinations of linguistically motivated approaches to address these problems in English-to-German SMT and show that they are complementary to one another, but also that the popular verbal pre-ordering can cause problems on the morphological and lexical level. A discriminative classifier can overcome these problems, in particular when enriching standard lexical features with features geared towards verbal inflection.

null

57,417

inproceedings

luong-etal-2017-machine

Machine Translation of {S}panish Personal and Possessive Pronouns Using Anaphora Probabilities

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2100/

Luong, Ngoc Quang and Popescu-Belis, Andrei and Rios Gonzales, Annette and Tuggener, Don

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

631--636

We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as {\textquoteleft}ella' vs. {\textquoteleft}she'/{\textquoteleft}it' or {\textquoteleft}su' vs. {\textquoteleft}his'/{\textquoteleft}her'/{\textquoteleft}its'/{\textquoteleft}their'. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83{\%}.

null

57,418

inproceedings

calixto-etal-2017-using

Using Images to Improve Machine-Translating {E}-Commerce Product Listings.

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2101/

Calixto, Iacer and Stein, Daniel and Matusov, Evgeny and Lohar, Pintu and Castilho, Sheila and Way, Andy

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

637--643

In this paper we study the impact of using images to machine-translate user-generated e-commerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multi-modal NMT models for re-ranking $n$-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.

null

57,419

inproceedings

ostling-tiedemann-2017-continuous

Continuous multilinguality with language vectors

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2102/

{\"Ostling, Robert and Tiedemann, J{\"org

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

644--649

Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.

null

57,420

inproceedings

kim-etal-2017-unsupervised

Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2103/

Kim, Yunsu and Schamper, Julian and Ney, Hermann

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

650--656

We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.

null

57,421

inproceedings

rios-gonzales-tuggener-2017-co

Co-reference Resolution of Elided Subjects and Possessive Pronouns in {S}panish-{E}nglish Statistical Machine Translation

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2104/

Rios Gonzales, Annette and Tuggener, Don

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

657--662

This paper presents a straightforward method to integrate co-reference information into phrase-based machine translation to address the problems of i) elided subjects and ii) morphological underspecification of pronouns when translating from pro-drop languages. We evaluate the method for the language pair Spanish-English and find that translation quality improves with the addition of co-reference information.

null

57,422

inproceedings

xia-etal-2017-large

Large-Scale Categorization of {J}apanese Product Titles Using Neural Attention Models

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2105/

Xia, Yandi and Levine, Aaron and Das, Pradipto and Di Fabbrizio, Giuseppe and Shinzato, Keiji and Datta, Ankur

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

663--668

We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories. Compared to a state-of-the-art Gradient Boosted Tree (GBT) classifier, the proposed model reduces training time from three weeks to three days while maintaining more than 96{\%} accuracy. Additionally, our proposed model characterizes products by imputing attentive focus on word tokens in a language agnostic way. The attention words have been observed to be semantically highly correlated with the predicted categories and give us a choice of automatic feature extraction for downstream processing.

null

57,423

inproceedings

shrestha-etal-2017-convolutional

Convolutional Neural Networks for Authorship Attribution of Short Texts

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2106/

Shrestha, Prasha and Sierra, Sebastian and Gonz{\'a}lez, Fabio and Montes, Manuel and Rosso, Paolo and Solorio, Thamar

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

669--674

We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.

null

57,424

inproceedings

yang-etal-2017-aspect

Aspect Extraction from Product Reviews Using Category Hierarchy Information

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2107/

Yang, Yinfei and Chen, Cen and Qiu, Minghui and Bao, Forrest

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

675--680

Aspect extraction abstracts the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of sub-categories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10{\%}.

null

57,425

inproceedings

soler-company-wanner-2017-relevance

On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2108/

Soler-Company, Juan and Wanner, Leo

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

681--687

The majority of approaches to author profiling and author identification focus mainly on lexical features, i.e., on the content of a text. We argue that syntactic and discourse features play a significantly more prominent role than they were given in the past. We show that they achieve state-of-the-art performance in author and gender identification on a literary corpus while keeping the feature set small: the used feature set is composed of only 188 features and still outperforms the winner of the PAN 2014 shared task on author verification in the literary genre.

null

57,426

inproceedings

glavas-etal-2017-unsupervised

Unsupervised Cross-Lingual Scaling of Political Texts

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2109/

Glava{\v{s}}, Goran and Nanni, Federico and Ponzetto, Simone Paolo

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

688--693

Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.

null

57,427

inproceedings

dernoncourt-etal-2017-neural

Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2110/

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

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

694--700

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model outperforms the state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.

null

57,428

inproceedings

sorodoc-etal-2017-multimodal

Multimodal Topic Labelling

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2111/

Sorodoc, Ionut and Lau, Jey Han and Aletras, Nikolaos and Baldwin, Timothy

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

701--706

Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose a multimodal approach to topic labelling using a simple feedforward neural network. Given a topic and a candidate image or textual label, our method automatically generates a rating for the label, relative to the topic. Experiments show that this multimodal approach outperforms single-modality topic labelling systems.

null

57,429

inproceedings

yang-etal-2017-detecting

Detecting (Un)Important Content for Single-Document News Summarization

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2112/

Yang, Yinfei and Bao, Forrest and Nenkova, Ani

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

707--712

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the {\textquotedblleft}beginning of document{\textquotedblright} heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.

null

57,430

inproceedings

he-sun-2017-f

{F}-Score Driven Max Margin Neural Network for Named Entity Recognition in {C}hinese Social Media

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2113/

He, Hangfeng and Sun, Xu

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

713--718

We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44{\%} improvement over previous state-of-the-art result.

null

57,431

inproceedings

bonadiman-etal-2017-effective

Effective shared representations with Multitask Learning for Community Question Answering

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2115/

Bonadiman, Daniele and Uva, Antonio and Moschitti, Alessandro

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

726--732

An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features. Unfortunately, DNNs usually require a lot of training data, especially for highly semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20{\%} of relative improvement over standard DNNs.

null

57,433

inproceedings

song-lee-2017-learning

Learning User Embeddings from Emails

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2116/

Song, Yan and Lee, Chia-Jung

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

733--738

Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was overlooked. In this paper, we propose to represent users using embeddings that are trained to reflect the email communication network. Our experiments on Enron dataset suggest that the resulting embeddings capture the semantic distance between users. To assess the quality of embeddings in a real-world application, we carry out auto-foldering task where the lexical representation of an email is enriched with user embedding features. Our results show that folder prediction accuracy is improved when embedding features are present across multiple settings.

null

57,434

inproceedings

dligach-etal-2017-neural

Neural Temporal Relation Extraction

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2118/

Dligach, Dmitriy and Miller, Timothy and Lin, Chen and Bethard, Steven and Savova, Guergana

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

746--751

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.

null

57,436

inproceedings

karn-etal-2017-end

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2119/

Karn, Sanjeev and Waltinger, Ulli and Sch{\"utze, Hinrich

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

752--758

We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.

null

57,437

inproceedings

cotterell-etal-2017-neural

Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion

Lapata, Mirella and Blunsom, Phil and Koller, Alexander

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-2120/

Cotterell, Ryan and Sylak-Glassman, John and Kirov, Christo

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

759--765

Many of the world`s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages.

null

57,438

inproceedings

minock-2017-cover

{COVER}: Covering the Semantically Tractable Questions

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3001/

Minock, Michael

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

1--4

In semantic parsing, natural language questions map to expressions in a meaning representation language (MRL) over some fixed vocabulary of predicates. To do this reliably, one must guarantee that for a wide class of natural language questions (the so called semantically tractable questions), correct interpretations are always in the mapped set of possibilities. In this demonstration, we introduce the system COVER which significantly clarifies, revises and extends the basic notion of semantic tractability. COVER achieves coverage of 89{\%} while the earlier PRECISE system achieved coverage of 77{\%} on the well known GeoQuery corpus. Like PRECISE, COVER requires only a simple domain lexicon and integrates off-the-shelf syntactic parsers. Beyond PRECISE, COVER also integrates off-the-shelf theorem provers to provide more accurate results. COVER is written in Python and uses the NLTK.

null

57,440

inproceedings

uszkoreit-etal-2017-common

Common Round: Application of Language Technologies to Large-Scale Web Debates

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3002/

Uszkoreit, Hans and Gabryszak, Aleksandra and Hennig, Leonhard and Steffen, J{\"org and Ai, Renlong and Busemann, Stephan and Dehdari, Jon and van Genabith, Josef and Heigold, Georg and Rethmeier, Nils and Rubino, Raphael and Schmeier, Sven and Thomas, Philippe and Wang, He and Xu, Feiyu

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

5--8

Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking. However, it is challenging to organize, structure, and navigate a vast number of diverse argumentations and comments collected from many participants over a long time period. In this paper we demonstrate Common Round, a next generation platform for large-scale web debates, which provides functions for eliciting the semantic content and structures from the contributions of participants. In particular, Common Round applies language technologies for the extraction of semantic essence from textual input, aggregation of the formulated opinions and arguments. The platform also provides a cross-lingual access to debates using machine translation.

null

57,441

inproceedings

list-2017-web

A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3003/

List, Johann-Mattis

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

9--12

The paper presents the Etymological DICtionary ediTOR (EDICTOR), a free, interactive, web-based tool designed to aid historical linguists in creating, editing, analysing, and publishing etymological datasets. The EDICTOR offers interactive solutions for important tasks in historical linguistics, including facilitated input and segmentation of phonetic transcriptions, quantitative and qualitative analyses of phonetic and morphological data, enhanced interfaces for cognate class assignment and multiple word alignment, and automated evaluation of regular sound correspondences. As a web-based tool written in JavaScript, the EDICTOR can be used in standard web browsers across all major platforms.

null

57,442

inproceedings

kiesel-etal-2017-wat

{WAT}-{SL}: A Customizable Web Annotation Tool for Segment Labeling

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3004/

Kiesel, Johannes and Wachsmuth, Henning and Al-Khatib, Khalid and Stein, Benno

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

13--16

A frequent type of annotations in text corpora are labeled text segments. General-purpose annotation tools tend to be overly comprehensive, often making the annotation process slower and more error-prone. We present WAT-SL, a new web-based tool that is dedicated to segment labeling and highly customizable to the labeling task at hand. We outline its main features and exemplify how we used it for a crowdsourced corpus with labeled argument units.

null

57,443

inproceedings

uslu-etal-2017-textimager

{T}ext{I}mager as a Generic Interface to {R}

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3005/

Uslu, Tolga and Hemati, Wahed and Mehler, Alexander and Baumartz, Daniel

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

17--20

R is a very powerful framework for statistical modeling. Thus, it is of high importance to integrate R with state-of-the-art tools in NLP. In this paper, we present the functionality and architecture of such an integration by means of TextImager. We use the OpenCPU API to integrate R based on our own R-Server. This allows for communicating with R-packages and combining them with TextImager`s NLP-components.

null

57,444

inproceedings

cabrera-etal-2017-grawitas

{G}ra{W}i{T}as: a Grammar-based {W}ikipedia Talk Page Parser

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3006/

Cabrera, Benjamin and Steinert, Laura and Ross, Bj{\"orn

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

21--24

Wikipedia offers researchers unique insights into the collaboration and communication patterns of a large self-regulating community of editors. The main medium of direct communication between editors of an article is the article`s talk page. However, a talk page file is unstructured and therefore difficult to analyse automatically. A few parsers exist that enable its transformation into a structured data format. However, they are rarely open source, support only a limited subset of the talk page syntax {--} resulting in the loss of content {--} and usually support only one export format. Together with this article we offer a very fast, lightweight, open source parser with support for various output formats. In a preliminary evaluation it achieved a high accuracy. The parser uses a grammar-based approach {--} offering a transparent implementation and easy extensibility.

null

57,445

inproceedings

nozza-etal-2017-twine

{TWINE}: A real-time system for {TW}eet analysis via {IN}formation Extraction

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3007/

Nozza, Debora and Ristagno, Fausto and Palmonari, Matteo and Fersini, Elisabetta and Manchanda, Pikakshi and Messina, Enza

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

25--28

In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at \url{http://twine-mind.cloudapp.net/streaming}.

null

57,446

inproceedings

gontrum-etal-2017-alto

{A}lto: Rapid Prototyping for Parsing and Translation

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3008/

Gontrum, Johannes and Groschwitz, Jonas and Koller, Alexander and Teichmann, Christoph

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

29--32

We present Alto, a rapid prototyping tool for new grammar formalisms. Alto implements generic but efficient algorithms for parsing, translation, and training for a range of monolingual and synchronous grammar formalisms. It can easily be extended to new formalisms, which makes all of these algorithms immediately available for the new formalism.

null

57,447

inproceedings

boros-etal-2017-cassandra

{CASSANDRA}: A multipurpose configurable voice-enabled human-computer-interface

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3009/

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

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

33--36

Voice enabled human computer interfaces (HCI) that integrate automatic speech recognition, text-to-speech synthesis and natural language understanding have become a commodity, introduced by the immersion of smart phones and other gadgets in our daily lives. Smart assistants are able to respond to simple queries (similar to text-based question-answering systems), perform simple tasks (call a number, reject a call etc.) and help organizing appointments. With this paper we introduce a newly created process automation platform that enables the user to control applications and home appliances and to query the system for information using a natural voice interface. We offer an overview of the technologies that enabled us to construct our system and we present different usage scenarios in home and office environments.

null

57,448

inproceedings

thorne-vlachos-2017-extensible

An Extensible Framework for Verification of Numerical Claims

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3010/

Thorne, James and Vlachos, Andreas

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

37--40

In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge. We focused on simple numerical claims such as {\textquotedblleft}population of Germany in 2015 was 80 million{\textquotedblright} which comprised a quarter of the test instances in the challenge, achieving 68{\%} accuracy. Our system extends previous work on semantic parsing and claim identification to handle temporal expressions and knowledge bases consisting of multiple tables, while relying solely on automatically generated training data. We demonstrate the extensible nature of our system by evaluating it on relations used in previous work. We make our system publicly available so that it can be used and extended by the community.

null

57,449

inproceedings

vallejo-huanga-etal-2017-adocs

{AD}o{CS}: Automatic Designer of Conference Schedules

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3011/

Vallejo Huanga, Diego Fernando and Morillo Alc{\'i}var, Paulina Adriana and Ferri Ram{\'i}rez, C{\`e}sar

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

41--44

Distributing papers into sessions in scientific conferences is a task consisting in grouping papers with common topics and considering the size restrictions imposed by the conference schedule. This problem can be seen as a semi-supervised clustering of scientific papers based on their features. This paper presents a web tool called ADoCS that solves the problem of configuring conference schedules by an automatic clustering of articles by similarity using a new algorithm considering size constraints.

null

57,450

inproceedings

gamallo-etal-2017-web

A Web Interface for Diachronic Semantic Search in {S}panish

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3012/

Gamallo, Pablo and Rodr{\'i}guez-Torres, Iv{\'a}n and Garcia, Marcos

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

45--48

This article describes a semantic system which is based on distributional models obtained from a chronologically structured language resource, namely Google Books Syntactic Ngrams. The models were created using dependency-based contexts and a strategy for reducing the vector space, which consists in selecting the more informative and relevant word contexts. The system allowslinguists to analize meaning change of Spanish words in the written language across time.

null

57,451

inproceedings

perez-cuadros-2017-multilingual

Multilingual {CALL} Framework for Automatic Language Exercise Generation from Free Text

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3013/

Perez, Naiara and Cuadros, Montse

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

49--52

This paper describes a web-based application to design and answer exercises for language learning. It is available in Basque, Spanish, English, and French. Based on open-source Natural Language Processing (NLP) technology such as word embedding models and word sense disambiguation, the application enables users to automatic create easily and in real time three types of exercises, namely, Fill-in-the-Gaps, Multiple Choice, and Shuffled Sentences questionnaires. These are generated from texts of the users' own choice, so they can train their language skills with content of their particular interest.

null

57,452

inproceedings

henrich-lang-2017-audience

Audience Segmentation in Social Media

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3014/

Henrich, Verena and Lang, Alexander

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

53--56

Understanding the social media audience is becoming increasingly important for social media analysis. This paper presents an approach that detects various audience attributes, including author location, demographics, behavior and interests. It works both for a variety of social media sources and for multiple languages. The approach has been implemented within IBM Watson Analytics for Social Media and creates author profiles for more than 300 different analysis domains every day.

null

57,453

inproceedings

da-cunha-etal-2017-artext

The ar{T}ext prototype: An automatic system for writing specialized texts

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3015/

da Cunha, Iria and Montan{\'e}, M. Amor and Hysa, Luis

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

57--60

This article describes an automatic system for writing specialized texts in Spanish. The arText prototype is a free online text editor that includes different types of linguistic information. It is designed for a variety of end users and domains, including specialists and university students working in the fields of medicine and tourism, and laypersons writing to the public administration. ArText provides guidance on how to structure a text, prompts users to include all necessary contents in each section, and detects lexical and discourse problems in the text.

null

57,454

inproceedings

dalvi-etal-2017-qcri

{QCRI} Live Speech Translation System

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3016/

Dalvi, Fahim and Zhang, Yifan and Khurana, Sameer and Durrani, Nadir and Sajjad, Hassan and Abdelali, Ahmed and Mubarak, Hamdy and Ali, Ahmed and Vogel, Stephan

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

61--64

This paper presents QCRI`s Arabic-to-English live speech translation system. It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network (TDNN) architecture, while our Machine Translation (MT) system uses both phrase-based and neural frameworks. Although our neural MT system is slower than the phrase-based system, it produces significantly better translations and is memory efficient. The demo is available at \url{https://st.qcri.org/demos/livetranslation}.

null

57,455

inproceedings

moreno-ortiz-2017-lingmotif

{L}ingmotif: Sentiment Analysis for the Digital Humanities

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3019/

Moreno-Ortiz, Antonio

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

73--76

Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application`s rich core lexicons, and employs context rules to account for sentiment shifters. It offers easy-to-interpret visual representations of quantitative data (text polarity, sentiment intensity, sentiment profile), as well as a detailed, qualitative analysis of the text in terms of its sentiment. Lingmotif can also take user-provided plugin lexicons in order to account for domain-specific sentiment expression. Lingmotif currently analyzes English and Spanish texts.

null

57,458

inproceedings

menini-etal-2017-ramble

{RAMBLE} {ON}: Tracing Movements of Popular Historical Figures

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3020/

Menini, Stefano and Sprugnoli, Rachele and Moretti, Giovanni and Bignotti, Enrico and Tonelli, Sara and Lepri, Bruno

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

77--80

We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century.

null

57,459

inproceedings

torr-2017-autobank

{A}utobank: a semi-automatic annotation tool for developing deep {M}inimalist {G}rammar treebanks

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3021/

Torr, John

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

81--86

This paper presents Autobank, a prototype tool for constructing a wide-coverage Minimalist Grammar (MG) (Stabler 1997), and semi-automatically converting the Penn Treebank (PTB) into a deep Minimalist treebank. The front end of the tool is a graphical user interface which facilitates the rapid development of a seed set of MG trees via manual reannotation of PTB preterminals with MG lexical categories. The system then extracts various dependency mappings between the source and target trees, and uses these in concert with a non-statistical MG parser to automatically reannotate the rest of the corpus. Autobank thus enables deep treebank conversions (and subsequent modifications) without the need for complex transduction algorithms accompanied by cascades of ad hoc rules; instead, the locus of human effort falls directly on the task of grammar construction itself.

null

57,460

inproceedings

galitsky-ilvovsky-2017-chatbot

Chatbot with a Discourse Structure-Driven Dialogue Management

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3022/

Galitsky, Boris and Ilvovsky, Dmitry

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

87--90

We build a chat bot with iterative content exploration that leads a user through a personalized knowledge acquisition session. The chat bot is designed as an automated customer support or product recommendation agent assisting a user in learning product features, product usability, suitability, troubleshooting and other related tasks. To control the user navigation through content, we extend the notion of a linguistic discourse tree (DT) towards a set of documents with multiple sections covering a topic. For a given paragraph, a DT is built by DT parsers. We then combine DTs for the paragraphs of documents to form what we call extended DT, which is a basis for interactive content exploration facilitated by the chat bot. To provide cohesive answers, we use a measure of rhetoric agreement between a question and an answer by tree kernel learning of their DTs.

null

57,461

inproceedings

marsi-etal-2017-marine

Marine Variable Linker: Exploring Relations between Changing Variables in Marine Science Literature

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3023/

Marsi, Erwin and Pinar {\O}zturk, Pinar and V. Ardelan, Murat

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

91--94

We report on a demonstration system for text mining of literature in marine science and related disciplines. It automatically extracts variables ({\textquotedblleft}CO2{\textquotedblright}) involved in events of change/increase/decrease ({\textquotedblleft}increasing CO2{\textquotedblright}), as well as co-occurrence and causal relations among these events ({\textquotedblleft}increasing CO2 causes a decrease in pH in seawater{\textquotedblright}), resulting in a big knowledge graph. A web-based graphical user interface targeted at marine scientists facilitates searching, browsing and visualising events and their relations in an interactive way.

null

57,462

inproceedings

cartier-2017-neoveille

Neoveille, a Web Platform for Neologism Tracking

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3024/

Cartier, Emmanuel

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

95--98

This paper details a software designed to track neologisms in seven languages through newspapers monitor corpora. The platform combines state-of-the-art processes to track linguistic changes and a web platform for linguists to create and manage their corpora, accept or reject automatically identified neologisms, describe linguistically the accepted neologisms and follow their lifecycle on the monitor corpora. In the following, after a short state-of-the-art in Neologism Retrieval, Analysis and Life-tracking, we describe the overall architecture of the system. The platform can be freely browsed at \url{www.neoveille.org} where detailed presentation is given. Access to the editing modules is available upon request.

null

57,463

inproceedings

kutuzov-kuzmenko-2017-building

Building Web-Interfaces for Vector Semantic Models with the {W}eb{V}ectors Toolkit

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3025/

Kutuzov, Andrey and Kuzmenko, Elizaveta

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

99--103

In this demo we present WebVectors, a free and open-source toolkit helping to deploy web services which demonstrate and visualize distributional semantic models (widely known as word embeddings). WebVectors can be useful in a very common situation when one has trained a distributional semantics model for one`s particular corpus or language (tools for this are now widespread and simple to use), but then there is a need to demonstrate the results to general public over the Web. We show its abilities on the example of the living web services featuring distributional models for English, Norwegian and Russian.

null

57,464

inproceedings

ferrero-etal-2017-intoevents

{I}n{T}o{E}vent{S}: An Interactive Toolkit for Discovering and Building Event Schemas

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3026/

Ferrero, Germ{\'a}n and Primadhanty, Audi and Quattoni, Ariadna

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

104--107

Event Schema Induction is the task of learning a representation of events (e.g., bombing) and the roles involved in them (e.g, victim and perpetrator). This paper presents InToEventS, an interactive tool for learning these schemas. InToEventS allows users to explore a corpus and discover which kind of events are present. We show how users can create useful event schemas using two interactive clustering steps.

null

57,465

inproceedings

vuppuluri-etal-2017-ice

{ICE}: Idiom and Collocation Extractor for Research and Education

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3027/

Vuppuluri, Vasanthi and Baki, Shahryar and Nguyen, An and Verma, Rakesh

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

108--111

Collocation and idiom extraction are well-known challenges with many potential applications in Natural Language Processing (NLP). Our experimental, open-source software system, called ICE, is a python package for flexibly extracting collocations and idioms, currently in English. It also has a competitive POS tagger that can be used alone or as part of collocation/idiom extraction. ICE is available free of cost for research and educational uses in two user-friendly formats. This paper gives an overview of ICE and its performance, and briefly describes the research underlying the extraction algorithms.

null

57,466

inproceedings

yoneda-etal-2017-bib2vec

{B}ib2vec: Embedding-based Search System for Bibliographic Information

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3028/

Yoneda, Takuma and Mori, Koki and Miwa, Makoto and Sasaki, Yutaka

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

112--115

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results.

null

57,467

inproceedings

liepins-etal-2017-summa

The {SUMMA} Platform Prototype

Martins, Andr{\'e} and Pe{\~n}as, Anselmo

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-3029/

Liepins, Renars and Germann, Ulrich and Barzdins, Guntis and Birch, Alexandra and Renals, Steve and Weber, Susanne and van der Kreeft, Peggy and Bourlard, Herv{\'e} and Prieto, Jo{\~a}o and Klejch, Ond{\v{r}}ej and Bell, Peter and Lazaridis, Alexandros and Mendes, Alfonso and Riedel, Sebastian and Almeida, Mariana S. C. and Balage, Pedro and Cohen, Shay B. and Dwojak, Tomasz and Garner, Philip N. and Giefer, Andreas and Junczys-Dowmunt, Marcin and Imran, Hina and Nogueira, David and Ali, Ahmed and Miranda, Sebasti{\~a}o and Popescu-Belis, Andrei and Miculicich Werlen, Lesly and Papasarantopoulos, Nikos and Obamuyide, Abiola and Jones, Clive and Dalvi, Fahim and Vlachos, Andreas and Wang, Yang and Tong, Sibo and Sennrich, Rico and Pappas, Nikolaos and Narayan, Shashi and Damonte, Marco and Durrani, Nadir and Khurana, Sameer and Abdelali, Ahmed and Sajjad, Hassan and Vogel, Stephan and Sheppey, David and Hernon, Chris and Mitchell, Jeff

Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

116--119

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

null

57,468

inproceedings

van-miltenburg-2017-pragmatic

Pragmatic descriptions of perceptual stimuli

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4001/

van Miltenburg, Emiel

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

1--10

This research proposal discusses pragmatic factors in image description, arguing that current automatic image description systems do not take these factors into account. I present a general model of the human image description process, and propose to study this process using corpus analysis, experiments, and computational modeling. This will lead to a better characterization of human image description behavior, providing a road map for future research in automatic image description, and the automatic description of perceptual stimuli in general.

null

57,470

inproceedings

barteld-2017-detecting

Detecting spelling variants in non-standard texts

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4002/

Barteld, Fabian

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

11--22

Spelling variation in non-standard language, e.g. computer-mediated communication and historical texts, is usually treated as a deviation from a standard spelling, e.g. 2mr as an non-standard spelling for tomorrow. Consequently, in normalization {--} the standard approach of dealing with spelling variation {--} so-called non-standard words are mapped to their corresponding standard words. However, there is not always a corresponding standard word. This can be the case for single types (like emoticons in computer-mediated communication) or a complete language, e.g. texts from historical languages that did not develop to a standard variety. The approach presented in this thesis proposal deals with spelling variation in absence of reference to a standard. The task is to detect pairs of types that are variants of the same morphological word. An approach for spelling-variant detection is presented, where pairs of potential spelling variants are generated with Levenshtein distance and subsequently filtered by supervised machine learning. The approach is evaluated on historical Low German texts. Finally, further perspectives are discussed.

null

57,471

inproceedings

marrese-taylor-matsuo-2017-replication

Replication issues in syntax-based aspect extraction for opinion mining

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4003/

Marrese-Taylor, Edison and Matsuo, Yutaka

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

23--32

Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.

null

57,472

inproceedings

pyatkin-webber-2017-discourse

Discourse Relations and Conjoined {VP}s: Automated Sense Recognition

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4004/

Pyatkin, Valentina and Webber, Bonnie

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

33--42

Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (\textit{inter-sentential}) and within sentences (\textit{intra-sentential}), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification pipeline for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser.

null

57,473

inproceedings

litvinova-etal-2017-deception

Deception detection in {R}ussian texts

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4005/

Litvinova, Olga and Seredin, Pavel and Litvinova, Tatiana and Lyell, John

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

43--52

Humans are known to detect deception in speech randomly and it is therefore important to develop tools to enable them to detect deception. The problem of deception detection has been studied for a significant amount of time, however the last 10-15 years have seen methods of computational linguistics being employed. Texts are processed using different NLP tools and then classified as deceptive/truthful using machine learning methods. While most research has been performed for English, Slavic languages have never been a focus of detection deception studies. The paper deals with deception detection in Russian narratives. It employs a specially designed corpus of truthful and deceptive texts on the same topic from each respondent

null

113. The texts were processed using Linguistic Inquiry and Word Count software that is used in most studies of text-based deception detection. The list of parameters computed using the software was expanded due to the designed users' dictionaries. A variety of text classification methods was employed. The accuracy of the model was found to depend on the author`s gender and text type (deceptive/truthful)." }

null

57,474

inproceedings

seminck-amsili-2017-computational

A Computational Model of Human Preferences for Pronoun Resolution

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4006/

Seminck, Olga and Amsili, Pascal

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

53--63

We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences.

null

57,475

inproceedings

piotrkowicz-etal-2017-automatic

Automatic Extraction of News Values from Headline Text

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4007/

Piotrkowicz, Alicja and Dimitrova, Vania and Markert, Katja

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

64--74

Headlines play a crucial role in attracting audiences' attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, large-scale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values - factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people`s decisions to click on a headline, supporting the need for an automatic news values detection.

null

57,476

inproceedings

chalaguine-schulz-2017-assessing

Assessing Convincingness of Arguments in Online Debates with Limited Number of Features

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4008/

Chalaguine, Lisa Andreevna and Schulz, Claudia

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

75--83

We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.

null

57,477

inproceedings

perez-melian-etal-2017-zipfs

{Z}ipf`s and {B}enford`s laws in {T}witter hashtags

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4009/

P{\'e}rez Meli{\'a}n, Jos{\'e} Alberto and Conejero, J. Alberto and Ferri Ram{\'i}rez, C{\`e}sar

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

84--93

Social networks have transformed communication dramatically in recent years through the rise of new platforms and the development of a new language of communication. This landscape requires new forms to describe and predict the behaviour of users in networks. This paper presents an analysis of the frequency distribution of hashtag popularity in Twitter conversations. Our objective is to determine if these frequency distribution follow some well-known frequency distribution that many real-life sets of numerical data satisfy. In particular, we study the similarity of frequency distribution of hashtag popularity with respect to Zipf`s law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford`s law, is a special case of Zipf`s law, a common pattern about the frequency distribution of leading digits. In order to compute correctly the frequency distribution of hashtag popularity, we need to correct many spelling errors that Twitter`s users introduce. For this purpose we introduce a new filter to correct hashtag mistake based on string distances. The experiments obtained employing datasets of Twitter streams generated under controlled conditions show that Benford`s law and Zipf`s law can be used to model hashtag frequency distribution.

null

57,478

inproceedings

amorim-veloso-2017-multi

A Multi-aspect Analysis of Automatic Essay Scoring for {B}razilian {P}ortuguese

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4010/

Amorim, Evelin and Veloso, Adriano

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

94--102

Several methods for automatic essay scoring (AES) for English language have been proposed. However, multi-aspect AES systems for other languages are unusual. Therefore, we propose a multi-aspect AES system to apply on a dataset of Brazilian Portuguese essays, which human experts evaluated according to five aspects defined by Brazilian Government to the National Exam to High School Student (ENEM). These aspects are skills that student must master and every skill is assessed apart from each other. Besides the prediction of each aspect, the feature analysis also was performed for each aspect. The AES system proposed employs several features already employed by AES systems for English language. Our results show that predictions for some aspects performed well with the features we employed, while predictions for other aspects performed poorly. Also, it is possible to note the difference between the five aspects in the detailed feature analysis we performed. Besides these contributions, the eight millions of enrollments every year for ENEM raise some challenge issues for future directions in our research.

null

57,479

inproceedings

ehren-2017-literal

Literal or idiomatic? Identifying the reading of single occurrences of {G}erman multiword expressions using word embeddings

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4011/

Ehren, Rafael

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

103--112

Non-compositional multiword expressions (MWEs) still pose serious issues for a variety of natural language processing tasks and their ubiquity makes it impossible to get around methods which automatically identify these kind of MWEs. The method presented in this paper was inspired by Sporleder and Li (2009) and is able to discriminate between the literal and non-literal use of an MWE in an unsupervised way. It is based on the assumption that words in a text form cohesive units. If the cohesion of these units is weakened by an expression, it is classified as literal, and otherwise as idiomatic. While Sporleder an Li used \textit{Normalized Google Distance} to modell semantic similarity, the present work examines the use of a variety of different word embeddings.

null

57,480

inproceedings

hatty-etal-2017-evaluating

Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction

Kunneman, Florian and I{\~n}urrieta, Uxoa and Camilleri, John J. and Ardanuy, Mariona Coll

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-4012/

H{\"atty, Anna and Dorna, Michael and Schulte im Walde, Sabine

Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics

113--121

Feature design and selection is a crucial aspect when treating terminology extraction as a machine learning classification problem. We designed feature classes which characterize different properties of terms based on distributions, and propose a new feature class for components of term candidates. By using random forests, we infer optimal features which are later used to build decision tree classifiers. We evaluate our method using the ACL RD-TEC dataset. We demonstrate the importance of the novel feature class for downgrading termhood which exploits properties of term components. Furthermore, our classification suggests that the identification of reliable term candidates should be performed successively, rather than just once.

null

57,481

inproceedings

nivre-etal-2017-universal

{U}niversal {D}ependencies

Klementiev, Alexandre and Specia, Lucia

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-5001/

Nivre, Joakim and Zeman, Daniel and Ginter, Filip and Tyers, Francis

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts

null

Universal Dependencies (UD) is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages. This tutorial gives an introduction to the UD framework and resources, from basic design principles to annotation guidelines and existing treebanks. We also discuss tools for developing and exploiting UD treebanks and survey applications of UD in NLP and linguistics.

null

57,482

inproceedings

sennrich-haddow-2017-practical

Practical Neural Machine Translation

Klementiev, Alexandre and Specia, Lucia

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-5002/

Sennrich, Rico and Haddow, Barry

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts

null

Neural Machine Translation (NMT) has achieved new breakthroughs in machine translation in recent years. It has dominated recent shared translation tasks in machine translation research, and is also being quickly adopted in industry. The technical differences between NMT and the previously dominant phrase-based statistical approach require that practictioners learn new best practices for building MT systems, ranging from different hardware requirements, new techniques for handling rare words and monolingual data, to new opportunities in continued learning and domain adaptation.This tutorial is aimed at researchers and users of machine translation interested in working with NMT. The tutorial will cover a basic theoretical introduction to NMT, discuss the components of state-of-the-art systems, and provide practical advice for building NMT systems.

null

57,483

inproceedings

vlachos-etal-2017-imitation

Imitation learning for structured prediction in natural language processing

Klementiev, Alexandre and Specia, Lucia

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-5003/

Vlachos, Andreas and Lampouras, Gerasimos and Riedel, Sebastian

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts

null

Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through \url{https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/}.

null

57,484

inproceedings

vulic-etal-2017-word

Word Vector Space Specialisation

Klementiev, Alexandre and Specia, Lucia

apr

2017

Valencia, Spain

Association for Computational Linguistics

https://aclanthology.org/E17-5004/

Vuli{\'c}, Ivan and Mrk{\v{s}}i{\'c}, Nikola and Pilehvar, Mohammad Taher

Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts

null

Specialising vector spaces to maximise their content with respect to one key property of vector space models (e.g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning. Such specialised vector spaces support different classes of NLP problems. Proposed approaches fall into two broad categories: a) Unsupervised methods which learn from raw textual corpora in more sophisticated ways (e.g. using context selection, extracting co-occurrence information from word patterns, attending over contexts); and b) Knowledge-base driven approaches which exploit available resources to encode external information into distributional vector spaces, injecting knowledge from semantic lexicons (e.g., WordNet, FrameNet, PPDB). In this tutorial, we will introduce researchers to state-of-the-art methods for constructing vector spaces specialised for a broad range of downstream NLP applications. We will deliver a detailed survey of the proposed methods and discuss best practices for intrinsic and application-oriented evaluation of such vector spaces.Throughout the tutorial, we will provide running examples reaching beyond English as the only (and probably the easiest) use-case language, in order to demonstrate the applicability and modelling challenges of current representation learning architectures in other languages.

null

57,485

inproceedings

arase-tsujii-2017-monolingual

Monolingual Phrase Alignment on Parse Forests

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1001/

Arase, Yuki and Tsujii, Junichi

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1--11

We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase correspondences. A dataset that provides gold parse trees and their phrase alignments is created. The experimental results confirm that the proposed method conducts highly accurate phrase alignment compared to human performance.

null

10.18653/v1/D17-1001

null

57,489

inproceedings

shi-etal-2017-fast

Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1002/

Shi, Tianze and Huang, Liang and Lee, Lillian

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

12--23

We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case $O(n^3)$ exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present $O(n^3)$ global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the {\textquotedblleft}second-best-in-class{\textquotedblright} result on the English Penn Treebank.

null

10.18653/v1/D17-1002

null

57,490

inproceedings

cao-etal-2017-quasi

Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1003/

Cao, Junjie and Huang, Sheng and Sun, Weiwei and Wan, Xiaojun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

24--34

We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs. Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic. These two characteristics make our algorithm relatively easy to be extended to incorporiate crossing-sensitive second-order features. We then introduce a new algorithm for quasi-second-order parsing. Experiments demonstrate that second-order features are helpful for Maximum Subgraph parsing.

null

10.18653/v1/D17-1003

null

57,491

inproceedings

zhang-etal-2017-position

Position-aware Attention and Supervised Data Improve Slot Filling

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1004/

Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

35--45

Organized relational knowledge in the form of {\textquotedblleft}knowledge graphs{\textquotedblright} is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2{\%} to 26.7{\%}.

null

10.18653/v1/D17-1004

null

57,492

inproceedings

liu-etal-2017-heterogeneous

Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1005/

Liu, Liyuan and Ren, Xiang and Zhu, Qi and Zhi, Shi and Gui, Huan and Ji, Heng and Han, Jiawei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

46--56

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.

null

10.18653/v1/D17-1005

null

57,493

inproceedings

wang-etal-2017-integrating

Integrating Order Information and Event Relation for Script Event Prediction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1006/

Wang, Zhongqing and Zhang, Yue and Chang, Ching-Yun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

57--67

There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.

null

10.18653/v1/D17-1006

null

57,494

inproceedings

tan-etal-2017-entity

Entity Linking for Queries by Searching {W}ikipedia Sentences

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1007/

Tan, Chuanqi and Wei, Furu and Ren, Pengjie and Lv, Weifeng and Zhou, Ming

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

68--77

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0{\%} in F1 on the ERD14 dataset and 56.9{\%} on the GERDAQ dataset.

null

10.18653/v1/D17-1007

null

57,495

inproceedings

pasini-navigli-2017-train

Train-{O}-{M}atic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1008/

Pasini, Tommaso and Navigli, Roberto

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

78--88

Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language`s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at \url{http://trainomatic.org}.

null

10.18653/v1/D17-1008

null

57,496

inproceedings

reddy-etal-2017-universal

Universal Semantic Parsing

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1009/

Reddy, Siva and T{\"ackstr{\"om, Oscar and Petrov, Slav and Steedman, Mark and Lapata, Mirella

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

89--101

Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.

null

10.18653/v1/D17-1009

null

57,497