pwc-archive/papers-with-abstracts huggingface dataset:
Measuring Frame Instance Relatedness
Authors: Valerio Basile, Roque Lopez Condori, Elena Cabrio
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Clustering, Reading Comprehension, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentiment Analysis

Frame semantics is a well-established framework to represent the meaning of natural language in computational terms. In this work, we aim to propose a quantitative measure of relatedness between pairs of frame instances. We test our method on a dataset of sentence pairs, highlighting the correlation between our metric and human judgments of semantic similarity. Furthermore, we propose an application of our measure for clustering frame instances to extract prototypical knowledge from natural language.
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition
Authors: Xia Cui, Sadamori Kojaku, Naoki Masuda, Danushka Bollegala
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Domain Adaptation, General Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Feature sparseness is a problem common to cross-domain and short-text classification tasks. To overcome this feature sparseness problem, we propose a novel method based on graph decomposition to find candidate features for expanding feature vectors. Specifically, we first create a feature-relatedness graph, which is subsequently decomposed into core-periphery (CP) pairs and use the peripheries as the expansion candidates of the cores. We expand both training and test instances using the computed related features and use them to train a text classifier. We observe that prioritising features that are common to both training and test instances as cores during the CP decomposition to further improve the accuracy of text classification. We evaluate the proposed CP-decomposition-based feature expansion method on benchmark datasets for cross-domain sentiment classification and short-text classification. Our experimental results show that the proposed method consistently outperforms all baselines on short-text classification tasks, and perform competitively with pivot-based cross-domain sentiment classification methods.
Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations
Authors: Nam Khanh Tran, Weiwei Cheng
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Abstract Meaning Representation, Learning Semantic Representations, Machine Translation, Relation Extraction, Sentence, Sentiment Analysis, Text Classification

Tree-structured LSTMs have shown advantages in learning semantic representations by exploiting syntactic information. Most existing methods model tree structures by bottom-up combinations of constituent nodes using the same shared compositional function and often making use of input word information only. The inability to capture the richness of compositionality makes these models lack expressive power. In this paper, we propose multiplicative tree-structured LSTMs to tackle this problem. Our model makes use of not only word information but also relation information between words. It is more expressive, as different combination functions can be used for each child node. In addition to syntactic trees, we also investigate the use of Abstract Meaning Representation in tree-structured models, in order to incorporate both syntactic and semantic information from the sentence. Experimental results on common NLP tasks show the proposed models lead to better sentence representation and AMR brings benefits in complex tasks.
What Analogies Reveal about Word Vectors and their Compositionality
Authors: Gregory Finley, Stephanie Farmer, Serguei Pakhomov
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Textual Similarity, Word Embeddings

Analogy completion via vector arithmetic has become a common means of demonstrating the compositionality of word embeddings. Previous work have shown that this strategy works more reliably for certain types of analogical word relationships than for others, but these studies have not offered a convincing account for why this is the case. We arrive at such an account through an experiment that targets a wide variety of analogy questions and defines a baseline condition to more accurately measure the efficacy of our system. We find that the most reliably solvable analogy categories involve either 1) the application of a morpheme with clear syntactic effects, 2) male{--}female alternations, or 3) named entities. These broader types do not pattern cleanly along a syntactic{--}semantic divide. We suggest instead that their commonality is distributional, in that the difference between the distributions of two words in any given pair encompasses a relatively small number of word types. Our study offers a needed explanation for why analogy tests succeed and fail where they do and provides nuanced insight into the relationship between word distributions and the theoretical linguistic domains of syntax and semantics.
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network
Authors: Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Negation, Semantic Textual Similarity, Word Embeddings

Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection
Authors: Yogarshi Vyas, Marine Carpuat
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Natural Language Inference, Question Answering

We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric relation between words. While previous work has focused on detecting hypernymy between word types, we ground the meaning of words in specific contexts drawn from WordNet examples, and require predictions to be sensitive to changes in contexts. WHiC lets us analyze complementary properties of two approaches of inducing vector representations of word meaning in context. We show that such contextualized word representations also improve detection of a wider range of semantic relations in context.
Domain-Specific New Words Detection in Chinese
Authors: Ao Chen, Maosong Sun
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8

With the explosive growth of Internet, more and more domain-specific environments appear, such as forums, blogs, MOOCs and etc. Domain-specific words appear in these areas and always play a critical role in the domain-specific NLP tasks. This paper aims at extracting Chinese domain-specific new words automatically. The extraction of domain-specific new words has two parts including both new words in this domain and the especially important words. In this work, we propose a joint statistical model to perform these two works simultaneously. Compared to traditional new words detection models, our model doesn{'}t need handcraft features which are labor intensive. Experimental results demonstrate that our joint model achieves a better performance compared with the state-of-the-art methods.
Deep Learning Models For Multiword Expression Identification
Authors: Waseem Gharbieh, Virendrakumar Bhavsar, Paul Cook
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Deep Learning, Information Retrieval, Machine Translation, Named Entity Recognition (NER), Opinion Mining, Sentence Classification, Text Generation

Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.
Mapping the Paraphrase Database to WordNet
Authors: Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Clustering

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89{\%} accuracy. The mapping also lays important groundwork for incorporating WordNet{'}s relations into PPDB so as to increase its utility for semantic reasoning in applications.
Semantic Frame Labeling with Target-based Neural Model
Authors: Yukun Feng, Dong Yu, Jian Xu, Chunhua Liu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, model, Sentence, Word Embeddings, Word Sense Disambiguation

This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model{'}s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible
Authors: Abhijeet Gupta, Gemma Boleda, Sebastian Pad{\'o}
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Knowledge Base Completion, Relation, Relation Extraction, Word Embeddings

Word embeddings are supposed to provide easy access to semantic relations such as {``}male of{''} (man{--}woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.
Comparing Approaches for Automatic Question Identification
Authors: Angel Maredia, Kara Schechtman, Sarah Ita Levitan, Julia Hirschberg
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Cross-corpus, Semantic Textual Similarity, Spoken Dialogue Systems, Word Embeddings

Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia. Typically, these corpora are labeled by human annotators, either in the lab or through crowd-sourcing; however, this is cumbersome and time-consuming for large corpora. We present four different approaches to automatically tagging a corpus when general topics of the conversations are known. We develop these approaches on the Columbia X-Cultural Deception corpus and find accuracy that significantly exceeds the baseline. Finally, we conduct a cross-corpus evaluation by testing the best performing approach on the Columbia/SRI/Colorado corpus.
Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for Croatian
Authors: Zoran Medi{\'c}, Jan {\v{S}}najder, Sebastian Pad{\'o}
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Textual Similarity

The Practical Lexical Function (PLF) model is a model of computational distributional semantics that attempts to strike a balance between expressivity and learnability in predicting phrase meaning and shows competitive results. We investigate how well the PLF carries over to free word order languages, given that it builds on observations of predicate-argument combinations that are harder to recover in free word order languages. We evaluate variants of the PLF for Croatian, using a new lexical substitution dataset. We find that the PLF works about as well for Croatian as for English, but demonstrate that its strength lies in modeling verbs, and that the free word order affects the less robust PLF variant.
The (too Many) Problems of Analogical Reasoning with Word Vectors
Authors: Anna Rogers, Aleks Drozd, R, Bofang Li
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Embeddings, Word Sense Disambiguation

This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such {``}linguistic regularities{''} as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity.
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions
Authors: Ekaterina Shutova, Andreas Wundsam, Helen Yannakoudakis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Clustering, Semantic Parsing, Sentence

Frame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP. However, to date these methods have relied on a predefined inventory of semantic roles. In this paper, we present a method to automatically learn argument role inventories for verbs from large corpora of text, images and videos. We evaluate the method against manually constructed role inventories in FrameNet and show that the visual model outperforms the language-only model and operates with a high precision.
Acquiring Predicate Paraphrases from News Tweets
Authors: Vered Shwartz, Gabriel Stanovsky, Ido Dagan
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Natural Language Inference, Question Answering

We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter. Analysis of the output of ten weeks of collection shows that the accuracy of paraphrases with different support levels is estimated between 60-86{\%}. We also demonstrate that our resource is to a large extent complementary to existing resources, providing many novel paraphrases. Our resource is publicly available, continuously expanding based on daily news.
Logical Metonymy in a Distributional Model of Sentence Comprehension
Authors: Emmanuele Chersoni, Aless Lenci, ro, Philippe Blache
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Retrieval, Sentence

In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e.g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e.g., writing). Psycholinguistic studies have revealed extra processing costs for logical metonymy, a phenomenon generally explained with the introduction of new semantic structure. In this paper, we present a general distributional model for sentence comprehension inspired by the Memory, Unification and Control model by Hagoort (2013,2016). We show that our distributional framework can account for the extra processing costs of logical metonymy and can identify the covert event in a classification task.
Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions
Authors: Jena D. Hwang, Archna Bhatia, Na-Rae Han, Tim O{'}Gorman, Vivek Srikumar, Nathan Schneider
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition{'}s lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition{'}s lexical function so they can be annotated at scale{---}supporting automatic, statistical processing of domain-general language{---}and discuss how this representation would allow for a simpler inventory of labels.
Issues of Mass and Count: Dealing with `Dual-Life' Nouns
Authors: Tibor Kiss, Francis Jeffry Pelletier, Halima Husi{\'c}, Johanna Poppek
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Philosophy

The topics of mass and count have been studied for many decades in philosophy (e.g., Quine, 1960; Pelletier, 1975), linguistics (e.g., McCawley, 1975; Allen, 1980; Krifka, 1991) and psychology (e.g., Middleton et al, 2004; Barner et al, 2009). More recently, interest from within computational linguistics has studied the issues involved (e.g., Pustejovsky, 1991; Bond, 2005; Schmidtke {\&} Kuperman, 2016), to name just a few. As is pointed out in these works, there are many difficult conceptual issues involved in the study of this contrast. In this article we study one of these issues {--} the {``}Dual-Life{''} of being simultaneously +mass and +count {--} by means of an unusual combination of human annotation, online lexical resources, and online corpora.
Parsing Graphs with Regular Graph Grammars
Authors: Sorcha Gilroy, Adam Lopez, Sebastian Maneth
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Machine Translation

Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages (HRL) have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. (2013) prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL are more expressive than is necessary to represent semantic graphs and we propose the use of Regular Graph Languages (RGL; Courcelle 1991), which is a subfamily of HRL, as a possible alternative. We provide a top-down parsing algorithm for RGL that runs in time linear in the size of the input graph.
Embedded Semantic Lexicon Induction with Joint Global and Local Optimization
Authors: Sujay Kumar Jauhar, Eduard Hovy
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Clustering, Question Answering, Semantic Parsing

Creating annotated frame lexicons such as PropBank and FrameNet is expensive and labor intensive. We present a method to induce an embedded frame lexicon in an minimally supervised fashion using nothing more than unlabeled predicate-argument word pairs. We hypothesize that aggregating such pair selectional preferences across training leads us to a global understanding that captures predicate-argument frame structure. Our approach revolves around a novel integration between a predictive embedding model and an Indian Buffet Process posterior regularizer. We show, through our experimental evaluation, that we outperform baselines on two tasks and can learn an embedded frame lexicon that is able to capture some interesting generalities in relation to hand-crafted semantic frames.
Generating Pattern-Based Entailment Graphs for Relation Extraction
Authors: Kathrin Eichler, Feiyu Xu, Hans Uszkoreit, Sebastian Krause
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Knowledge Base Population, Natural Language Inference, Relation, Relation Extraction

Relation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions. Deriving relation patterns automatically usually results in large numbers of candidates, which need to be filtered to derive a subset of patterns that reliably extract correct relation mentions. We address the pattern selection task by exploiting the knowledge represented by entailment graphs, which capture semantic relationships holding among the learned pattern candidates. This is motivated by the fact that a pattern may not express the target relation explicitly, but still be useful for extracting instances for which the relation holds, because its meaning entails the meaning of the target relation. We evaluate the usage of both automatically generated and gold-standard entailment graphs in a relation extraction scenario and present favorable experimental results, exhibiting the benefits of structuring and selecting patterns based on entailment graphs.
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention
Authors: Maria Becker, Michael Staniek, Vivi Nastase, Alexis Palmer, Anette Frank
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, General Classification, Language Modeling, Language Modelling, Relation Classification, Sentence Classification, Word Embeddings

Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.