WordNet

We discuss a method for augmenting and rearranging a structured lexicon in order to make it more suitable for a topic labefing task, by making use of lexical association information …...

This paper proposes benchmarks for systems of automatic sense identification. A textual corpus in which open-class words had been tagged both syntactically and semantically was used to explore three statistical …...

Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships …...

As the first step in an automated text sum- marization algorithm, this work presents a new method for automatically identi- fying the central ideas in a text based on a …...

In this paper, we present a new approach for word sense disambiguation (WSD) us- ing an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate …...

. This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use oil' the …...

Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each applica- tion. Although some semantic information is available in general-purpose knowledge …...

We analyze the results of a semantic an- notation task performed by novice taggers as part of the WordNet SemCor project (Landes et al., in press). Each polysemous content word …...

A layered approach to information retrieval permits the inclusion of multiple search en- gines as well as multiple databases, with a natural language layer to convert English queries for use …...

A layered approach to information retrieval permits the inclusion of multiple search en- gines as well as nmltiple databases, with a natural language layer to convert English queries for use …...

In this paper we specifically address questions of polysemy with respect to verbs, and how regular extensions of meaning can be achieved through the adjunction of particular syntactic phrases. We …...

This paper describes differents methods and tricks in connection with our program which has been entered in the Loebner Prize competition that will happen on Sunday 11 January 1998, at …...

Two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broaden- ing of …...

In this work, we use a large text corpus to order nouns by their level of specificity. This semantic information can for most nouns be determined with over 80% accuracy …...

A number of previous experiments on the role of lexical ambiguity, in Information Retrieval are re- produced on the'IR-Semcor test collection (derived from Semcor), where both queries and documents are …...

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from pre-existing lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We …...

A two-tier model for the description of morphologi- cal, syntactic and semantic variations of multi-word terms is presented. It is applied to term normal- ization of French and English corpora …...

Lexicon definition is one of the main bot- tlenecks in the development of new ap- plications in the field of Information Ex- traction from text. Generic resources (e.g., lexical databases) …...

This paper describes an automatic method for extracting systematic polysemy from a hierar- chically organized semantic lexicon (WordNet). Systematic polysemy is a set of word senses that are related in …...

This paper describes our actual and ongoing work in supporting semi-automatic ontology ac- quisition from a corporate intranet of an in- surance company. A comprehensive architec- ture and a system …...

This paper presents an algorithm for finding systematic polysemous classes in WordNet and similar semantic databases, based on a definition in (Apresjan 1973). The...

Our system for the SENSEVAL-2 all words task uses automatically acquired selectional prefer- ences to sense tag subject and object head nouns, along with the associated verbal pred- icates. The …...

Information about the animacy of nouns is important for a wide range of tasks in NLP. In this paper, we present a method for determining the animacy of English nouns …...

We present here the main ideas of the algo- rithm employed in the SMUls and SMU aw sys- tems. These systems have participated in the SENSEVAL-2 competition attaining the best …...

AbstractThe semantics of verbs implies, as is known, a great number of difficulties, when it is to be represented in a computational lexicon. The Slavic languages are especially challenging in …...

This paper describes IITl, IIT2, and IIT3, three versions of a semantic tagging system basing its sense discriminations on WordNet examples. The system uses WordNet relations aggressively, both in identifying …...

The issues in polysemy with respect to the verbs in WordNet will be discussed in this paper. The hypernymy/hyponymy structure of the multiple senses is observed when we try to …...

This paper presents an unsupervised method for assembling semantic knowledge from a part-of- speech tagged corpus using graph algorithms. The graph model is built by linking pairs of words which …...

Broad-coverage lexical resources such as WordNet are extremely useful. However, they often include many rare senses while missing domain-specific senses. We present a clustering algorithm called CBC (Cluster- ing By …...

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and …...

We describe an in-depth study of using a dictionary (WordNet) and web search engines (Altavista, MSN, and Google) to boost the performance of an automated question answering system, Webclopedia, in …...

Anaphora is a common phenomenon in discourses as well as an important research issue in the applications of natural language processing. In this paper, the anaphora resolution is achieved by …...

This paper introduces PhraseNet, a context- sensitive lexical semantic knowledge base sys- tem. Based on the supposition that seman- tic proximity is not simply a relation between two words in …...

We present an algorithm for generating referring expressions in open domains. Existing algorithms work at the semantic level and assume the avail- ability of a classification for attributes, which is …...

We have participated in both English all words 3.task and English lexical sample task of SENSEVAL- Our system disambiguates senses of a target word in a context by selecting a …...

We present a learning-based method to identify single-snippet answers to definition questions in question answering systems for document collections. Our method combines and extends two previous techniques that were based …...

We present a novel approach to spoken dialogue summarization. Our system employs a set of semantic similarity metrics using the noun por- tion of WordNet as a knowledge source. So …...

This paper introduces SENSELEARNER – a mini- mally supervised sense tagger that attempts to dis- ambiguate all content words in a text using the senses from WordNet. SENSELEARNER partici- pated …...

The SENSEVAL-3 task to perform word-sense disambiguation of WordNet glosses was designed to encourage development of technology to make use of standard lexical resources. The task was based on the …...

The DLSI-UA team is currently working on sev- eral word sense disambiguation approaches, both supervised and unsupervised. These approaches are based on different ways to use both annotated and unannotated …...

This paper presents Domain Relevance Estima- tion (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain …...

ion with HowNet Semantics Tony Veale Department of Computer Science, University College Dublin, Belfield, D4, Dublin, Ireland Tony.Veale@UCD.ie http://www.cs.ucd.ie/staff/tveale.html Abstract. One generally accepted hallmark of creative thinking is an ability …...

Research on document similarity has shown that complex representations are not more accurate than the simple bag-of- words. Term clustering, e.g. using latent semantic indexing, word co-occurrences or synonym relations …...

Extracting semantic relationships between en- tities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based rela- tion extraction using SVM. Our study illus- …...

In this paper, we present an unsupervised methodology for propagating lexical co- occurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching …...

The limited coverage of lexical-semantic re- sources is a significant problem for NLP sys- tems which can be alleviated by automati- cally classifying the unknown words. Su- persense tagging assigns …...

We present a novel approach for discov- ering word categories, sets of words shar- ing a significant aspect of their mean- ing. We utilize meta-patterns of high- frequency words and …...

Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences de- scribe. Each event has its own …...

Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised …...

Supervised word sense disambiguation has proven incredibly difficult. Despite significant effort, there has been little suc- cess at using contextual features to accu- rately assign the sense of a word. …...

This paper investigates an isolated setting of the lexical substitution task of replac- ing words with their synonyms. In par- ticular, we examine this problem in the setting of subtitle …...

Many of the tasks required for semantic tagging of phrases and texts rely on a list of words annotated with some semantic features. We present a method for ex- tracting …...

A web search with double checking model is proposed to explore the web as a live corpus. Five association measures including variants of Dice, Overlap Ratio, Jaccard, and Cosine, as …...

This paper describes an algorithm for propagating verb arguments along lexi- cal chains consisting of WordNet rela- tions. The algorithm creates verb argu- ment structures using VerbNet syntactic patterns. In …...

We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously …...

We describe Castanet, an algorithm for auto- matically generating hierarchical faceted meta- data from textual descriptions of items, to be in- corporated into browsing and navigation inter- faces for large …...

We present an approach for semantic rela- tion extraction between nominals that com- bines shallow and deep syntactic processing and semantic information using kernel meth- ods. Two information sources are …...

This paper describes the HIT system and its participation in SemEval-2007 English Lexical Substitution Task. Two main steps are included in our method: candidate sub- stitute extraction and candidate scoring. …...

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algo- rithms only need the WordNet and bigram counts, and does not require training. …...

It has been widely observed that different NLP appli- cations require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are …...

Many systems for tasks such as question answer- ing, multi-document summarization, and infor- mation retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are …...

Human categorization is neither a binary nor a context-free process. Rather, some con- cepts are better examples of a category than others, while the criteria for category mem- bership may …...

This paper presents an application of PageR- ank, a random-walk model originally de- vised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess …...

This paper presents a method for auto- matic classification of semantic relations between nominals using Sequential Minimal Optimization. We participated in the four categories of SEMEVAL task 4 (A: No …...

We propose a new method for organizing the numerous collocates into semantic thesaurus categories. The approach introduces a thesaurus-based semantic classification model automatically learning semantic relations for classifying adjective-noun (A-N) …...

This paper presents a method of acquiring knowledge from the Web for noun sense disambiguation. Words, called selectors, are acquired which take the place of an instance of a target …...

The WordNet verb hierarchy is tested, with a view to improving the perform- ance of its applications, revealing topo- logical anomalies and casting doubt on its semantic categories. Encoded tro- …...

. n-gram language modeling is a popular technique used to improve performance of various NLP applications. However, it still faces the “curse of dimensionality” issue wherein word sequences on which …...

The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Sum- marisation. In this paper, we …...

Current readability formulae have often been criticized for being unstable or not valid. They are mostly computed in regression analysis based on intuitively-chosen variables and graded readings. This study explores …...

Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In con- trast to Li and Roth (2002)’s approach which makes …...

. In this paper, we test a working hypothesis that there is a significant relationship between semantic similarity and subcategorization frames in English. This paper is under the assumption that …...

This study presents a novel approach to the problem of system portability across differ- ent domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set …...

This paper studies the application of text simi- larity methods to disambiguate ambiguous links between WordNet nouns and Wikipedia cate- gories. The methods range from word overlap between glosses, random …...

The use of lexical semantic knowledge in information retrieval has been a field of ac- tive study for a long time. Collaborative knowledge bases like Wikipedia and Wik- tionary, which …...

There have been considerable attempts to incorporate semantic knowledge into coreference resolution systems: different knowledge sources such as WordNet and Wikipedia have been used to boost the per- formance. In …...

We present a classifier to predict con- textual polarity of subjective phrases in a sentence. Our approach features lexi- cal scoring derived from the Dictionary of Affect in Language (DAL) …...

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fun- damental in linguistics and NLP. Manual concept compilation is labor intensive, er- ror prone and subjective. …...

. We introduce a method for learning to assign word senses to translation pairs. In our approach, this sense assignment or disambiguation problem is transformed into one on how to …...

In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract …...

We describe a method for creating a non- English subjectivity lexicon based on an English lexicon, an online translation ser- vice and a general purpose thesaurus: Wordnet. We use a …...

Sentiment analysis often relies on a se- mantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lex- icons, but they tend …...

We present a method for grouping the synonyms of a lemma according to its dictionary senses. The senses are defined by a large machine read- able dictionary for French, the …...

This paper introduces an integrative approach to automatic word sense subjectivity annota- tion. We use features that exploit the hier- archical structure and domain information in lexical resources such as …...

This paper presents a novel automatic approach to partially integrate FrameNet and WordNet. In that way we expect to extend FrameNet coverage, to en- rich WordNet with frame semantic information …...

In this paper, we introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, …...

A novel method for unsupervised acquisition of knowledge for taxonomies of concepts from raw Wikipedia text is presented. We assume that the concepts classified under the same node in a …...

We present a novel graph-based algo- rithm for the automated disambiguation of glosses in lexical knowledge resources. A dictionary graph is built starting from senses (vertices) and explicit or implicit …...

. In this paper, we introduce a minimally supervised method for learning to classify named-entity titles in a given encyclopedia into broad semantic categories in an existing ontology. Our main …...

AbstractDetermining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet …...

Although many algorithms have been devel- oped to harvest lexical resources, few organize the mined terms into taxonomies. We pro- pose (1) a semi-supervised algorithm that uses a root concept, …...

AbstractWe present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relation-denoting adjectives, a distinction that is highly relevant for ontology learning. The feasibility of this classification …...

AbstractThis paper presents an algorithm for aligning FrameNet lexical units to WordNet synsets. Both, FrameNet and WordNet, are well-known as well as widely-used resources by the entire research community. They …...

Text mining for global health surveillance is an emerging technology that is gaining increased attention from public health or- ganisations and governments. The lack of multilingual resources such as Word- …...

In this paper we present BabelNet – a very large, wide-coverage multilingual se- mantic network. The resource is automat- ically constructed by means of a method- ology that integrates lexicographic …...

There is significant evidence in the literature that integrating knowledge about multiword expressions can improve shallow parsing ac- curacy. We present an experimental study to quantify this improvement, focusing on …...

We describe two approaches for All-words Word Sense Disambiguation on a Spe- cific Domain. The first approach is a knowledge based approach which extracts domain-specific largest connected com- ponents from …...

AbstractThis paper presents a comparison of three computational approaches to selectional preferences: (i) an intuitive distributional approach that uses second-order co-occurrence of predicates and complement properties; (ii) an EM-based clustering …...

AbstractGraph-based similarity over WordNet has been previously shown to perform very well on word similarity. This paper presents a study of the performance of such a graph-based algorithm when using …...

AbstractThis paper presents a novel automatic approach to partially integrate FrameNet and WordNet. In that way we expect to extend FrameNet coverage, to enrich WordNet with frame semantic information and …...

AbstractLexical resources are basic components of many text processing system devoted to information extraction, question answering or dialogue. In paste years many resources have been developed such as FrameNet and …...

We describe a WordNet-based system for the extraction of semantic relations be- tween pairs of nominals appearing in English texts. The system adopts a lightweight approach, based on training a …...

We present the first evaluation of the utility of automatic evaluation metrics on surface real- izations of Penn Treebank data. Using outputs of the OpenCCG and XLE realizers, along with …...

AbstractIn ontology learning from texts, we have ontology-rich domains where we have large structured domain knowledge repositories or we have large general corpora with large general structured knowledge repositories such …...

We describe two systems that participated in SemEval-2010 task 17 (All-words Word Sense Disambiguation on a Specific Do- main) and were ranked in the third and fourth positions in the …...

AbstractIndia is a multilingual country where machine translation and cross lingual search are highly relevant problems. These problems require large resources- like wordnets and lexicons- of high quality and coverage. …...

One of the main obstacles to high- performance Word Sense Disambigua- tion (WSD) is the knowledge acquisi- tion bottleneck. In this paper, we present a methodology to automatically extend WordNet …...

AbstractThe paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model …...

AbstractIn this paper we discuss noun compounding, a highly generative, productive process, in three distinct languages: Czech, English and Zulu. Derivational morphology presents a large grey area between regular, compositional …...

AbstractThis paper presents Q-WordNet, a lexical resource consisting of WordNet senses automatically annotated by positive and negative polarity. Polarity classification amounts to decide whether a text (sense, sentence, etc.) may …...

AbstractIt is recognized that many evaluation metrics of machine translation in use that focus on surface word level suffer from their lack of tolerance of linguistic variance, and the incorporation …...

AbstractIn this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a …...

Name ambiguity problem has raised urgent demands for efficient, high-quality named ent- ity disambiguation methods. In recent years, the increasing availability of large-scale, rich semantic knowledge sources (such as Wikipe- …...

Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current state- of-the-art methods in taxonomy learning have disregarded word polysemy, in effect, …...

AbstractThe definition of lexical semantic similarity measures has been the subject of lots of works for many years. In this article, we focus more specifically on distributional semantic similarity measures. …...

AbstractDespite the large variety of corpora in the biomedical domain their annotations differ in many respects, e.g., the coverage of different, highly specialized knowledge domains, varying degrees of granularity of …...

. This paper presents and analyzes the KOLON System, created to facilitate Ko- rean Natural Language Processing (KNLP) and to improve experimental results through the KOLON Ontology. It is currently …...

This paper describes the UMCC-DLSI system in SemEval-2010 task number 17 (All-words Word Sense Disambiguation on Specific Domain). The main purpose of this work is to evaluate and compare our …...

This paper describes our system for the classification of argument coercion for SemEval-2010 Task 7. We present two ap- proaches to classifying an argument’s se- mantic class, which is then …...

In this paper we describe Mephisto, our system for Task 9 of the SemEval-2 work- shop. Our approach to this task is to de- velop a machine learning classifier which …...

AbstractWe describe results of a word sense annotation task using WordNet, involving half a dozen well-trained annotators on ten polysemous words for three parts of speech. One hundred sentences for …...

We combine relational and attributional similarity for the task of identifying instances of semantic relations, such as PRODUCT-PRODUCER and ORIGIN- ENTITY, between nominals in text. We use no pre-existing lexical …...

We present a novel approach for automatic collocation error correction in learner English which is based on paraphrases extracted from parallel corpora. Our key assumption is that collocation errors are …...

Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly …...

Traditional approaches to sentiment classifica- tion rely on lexical features, syntax-based fea- tures or a combination of the two. We pro- pose semantic features using word senses for a supervised …...

. The distinction between concrete and abstract concepts is psychologically valid but so far it can hardly be quantified in any objective way, which prevents it from being further studied …...

We re-investigate the rationale for and the ef- fectiveness of adopting the notions of depth and density in WordNet-based semantic sim- ilarity measures. We show that the intuition for including …...

In this paper, we propose a novel topic model based on incorporating dictionary definitions. Traditional topic models treat words as surface strings without assuming predefined knowledge about word mean- ing. …...

In this paper an unsupervised approach to do- main adaptation is presented, which exploits external knowledge sources in order to port a classification model into a new thematic do- main. …...

In this paper, we introduce a hybrid method to associate English collocations with sense class members chosen from WordNet. Our combinational approach includes a learning-based method, a paraphrase-based method and …...

AbstractAutomatic approaches to creating and extending wordnets, which have become very popular in the past decade, inadvertently result in noisy synsets. This is why we propose an approach to detect …...

AbstractNowadays, Wordnet is used in natural language processing as one of the major linguistic resources. Having such a resource for Persian language helps researchers in computational linguistics and natural language …...

AbstractThis paper presents a model to enrich an ontology with a thesaurus based on a domain corpus and WordNet. The model is applied to the data privacy domain and the …...

AbstractContributive resources, such as wikipedia, have proved to be valuable in Natural Language Processing or Multilingual Information Retrieval applications. This article focusses on Wiktionary, the dictionary part of the collaborative …...

Our system breaks down the problem of rank- ing a list of lexical substitutions according to how simple they are in a given context into a series of pairwise comparisons …...

AbstractWe analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text. Our study focuses on the MASC …...

AbstractThe WordNet knowledge model is currently implemented in multiple software frameworks providing procedural access to language instances of it. Frameworks tend to be focused on structural/design aspects of the model …...

AbstractThis paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation …...

Understanding and interpretation of nominal compounds has been a long-standing area of interest in NLP research for various rea- sons. (1) Nominal compounds occur fre- quently in most languages. (2) …...

This paper describes the participation of the IRIT team to SemEval 2012 Task 6 (Seman- tic Textual Similarity). The method used con- sists of a n-gram based comparison method combined …...

AbstractThis paper describes the connection of WordNet to a generic ontology based on DOLCE. We developed a complete set of heuristics for mapping all WordNet nouns, verbs and adjectives to …...

This paper describes our participation in the task denominated Cross-Lingual Textual En- tailment (CLTE) for content synchronization. We represent an approach to CLTE using machine translation to tackle the problem …...

AbstractThis paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional …...

We propose an unsupervised method for clus- tering the translations of a word, such that the translations in each cluster share a com- mon semantic sense. Words are assigned to …...

AbstractThis demo presents the second prototype of WordNet Atlas, a web application that gives users the ability to navigate and visualize the 146,312 word senses of the nouns contained within …...

AbstractIn this paper, we propose a simple methodology for building or extending wordnets using easily extractible lexical knowledge from Wiktionary and Wikipedia. This method relies on a large multilingual translation/synonym …...

In this paper, we propose a weakly supervised system, YouCat, for categorizing Youtube videos into different genres like Comedy, Horror, Romance, Sports and Technology The system takes a Youtube video …...

Wordnets are lexico-semantic resources essential in many NLP tasks. Princeton WordNet is the most widely known, and the most influential, among them. Word- nets for languages other than English tend …...

We introduce Peripheral Diversity (PD) as a knowledge-based approach to achieve multi- lingual Word Sense Disambiguation (WSD). PD exploits the frequency and diverse use of word senses in semantic subgraphs …...

Sentiment Analysis in Twitter has become an important task due to the huge user-generated content published over such media. Such analysis could be useful for many domains such as Marketing, …...

In this paper, we present the JUCSE system, designed for the TempEval-3 shared task. The system extracts events and temporal infor- mation from natural text in English. We have participated …...

In this paper we describe KnCe2013-CORE, a system to compute the semantic similarity of two short text snippets. The system com- putes a number of features which are gath- ered …...

This paper describes the specifications and re- sults of SSA-UO, unsupervised system, pre- sented in SemEval 2013 for Sentiment Analy- sis in Twitter (Task 2) (Wilson et al., 2013). The …...

Expensive feature engineering based on WordNet senses has been shown be useful for document level sentimentto classification. A plausible reason for such a performance improvement is the reduction in data …...

Studies of the graph of dictionary definitions (DD) (Picard et al., 2009; Levary et al., 2012) have revealed strong semantic coherence of local topological structures. The techniques used in these …...

We propose a new method for translation ac- quisition which uses a set of synonyms to ac- quire translations from comparable corpora. The motivation is that, given a certain query …...

This work introduces a new unsupervised approach to multilingual word sense disambiguation. Its main purpose is to automatically choose the intended sense (meaning) of a word in a particular context …...

WoNeF, an improved, extended and evaluated automatic French translation of WordNet Identifying the various possible meanings of each word of the vocabulary is a difficult problem that requires a lot …...

AbstractWe develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy …...

AbstractRecent studies in metaphor extraction across several languages (Broadwell et al., 2013; Strzalkowski et al., 2013) have shown that word imageability ratings are highly correlated with the presence of metaphors …...

AbstractThe present paper describes the construction of a resource to determine the lexical preference class of a large number of English noun-senses (≈ 14,000) with respect to the distinction between …...

AbstractIn computation linguistics a combination of syntagmatic and paradigmatic features is often exploited. While the first aspects are typically managed by information present in large n-gram databases, domain and ontological …...

This paper reports our submissions to the Cross Level Semantic Similarity (CLSS) task in SemEval 2014. We submitted one Random Forest regression system on each cross level text pair, i.e., …...

We present an effective modification of the popular Brown et al. 1992 word clustering algorithm, using a dependency language model. By leveraging syntax-based context, resulting clusters are better when evaluated …...

Collections of relational paraphrases have been automatically constructed from large text cor- pora, as a WordNet counterpart for the realm of binary predicates and their surface forms. How- ever, these …...

AbstractAnnotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game …...

AbstractThe growing amount of available information and the importance given to the access to technical information enhance the potential role of NLP applications in enabling users to deal with information …...

Aspect-based sentiment analysis estimates the sentiment expressed for each particu- lar aspect (e.g., battery, screen) of an en- tity (e.g., smartphone). Different words or phrases, however, may be used to …...

AbstractThis paper presents a method to create WordNet-like lexical resources for different languages. Instead of directly translating glosses from one language to another, we perform first semantic parsing of WordNet …...

AbstractThis paper presents the Predicate Matrix v1.1, a new lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. We start from …...

In this paper, we investigate the differ- ence between word and sense similarity measures and present means to convert a state-of-the-art word similarity measure into a sense similarity measure. In …...

In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. …...

This paper describes our submission to Se- mEval 2014 Task 41 (aspect based senti- ment analysis). The current work is based on the assumption that it could be advan- tageous …...

Inference about whether a word in one text has similar meaning to another word in the other text is an essential task in order to understand whether two texts have …...

We present the first large-scale English “all- words lexical substitution” corpus. The size of the corpus provides a rich resource for investigations into word meaning. We investigate the nature of …...

AbstractThe coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing. Automatic alignment of complementary resources is one way of …...

Interpreting event mentions in text is cen- tral to many tasks from scientific research to intelligence gathering. We present an event trigger detection system and explore baseline configurations. Specifically, we …...

A range of approaches to the representa- tion of lexical semantics have been explored within Computational Linguistics. Two of the most popular are distributional and knowledge- based models. This paper …...

Recent work has successfully leveraged the semantic information extracted from lexi- cal knowledge bases such as WordNet and FrameNet to improve English event corefer- ence resolvers. The lack of comparable …...

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figura- tive Language in Twitter. The proposed ap- proach employs syntactical and morphological features, which indicate sentiment polarity …...

Wordnet is a standard semantic resource for several Natural Language Processing tasks and it is available for an increas- ing number of languages. The Croatian Wordnet (CroWN) was a relatively …...

We propose FINET, a system for detect- ing the types of named entities in short inputs—such as sentences or tweets—with respect to WordNet’s super fine-grained type system. FINET generates candidate …...

Similarity between natural language texts, sentences in terms of meaning, known as tex- tual entailment, is a generic problem in the area of computational linguistics. In the last few years …...

This study focuses on personality predic- tion of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character …...

Random walks over large knowledge bases like WordNet have been successfully used in word similarity, relatedness and disambigua- tion tasks. Unfortunately, those algorithms are relatively slow for large repositories, with …...

This paper describes the first shared task on Taxonomy Extraction Evaluation organ- ised as part of SemEval-2015. Participants were asked to find hypernym-hyponym re- lations between given terms. For each …...

Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary. These con- cepts are often obtained by selecting …...

This paper describes the yiGou system we de- veloped to compute the semantic similarity of two English sentences, which we submitted to the SemEval 2015 Task 2 (English subtask). The …...

AbstractThis paper presents the Predicate Matrix 1.3, a lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. This new version of …...

AbstractFrame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, …...

AbstractWhen making clinical decisions, practitioners need to rely on the most relevant evidence available. However, accessing a vast body of medical evidence and confronting with the issue of information overload …...

Automatically generated databases of English paraphrases have the drawback that they re- turn a single list of paraphrases for an input word or phrase. This means that all senses of …...

AbstractWordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending …...

AbstractWe present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., Word-Net, FrameNet, Wiktionary) by linking them on …...

This paper describes our participation in the SemEval-2016 Task 1: Semantic Textual Sim- ilarity (STS). We developed three methods for the English subtask (STS Core). The first method is unsupervised …...

This paper presents a rule-based approach to constructing lexical axioms from Word- Net verb entries in an expressive semantic representation, Episodic Logic (EL). EL differs from other representations in be- …...

The ability to capture time information is essential to many natural language pro- cessing and information retrieval applica- tions. Therefore, a lexical resource asso- ciating word senses to their temporal …...

In this paper we present our system de- veloped for the SemEval 2016 Task 2 - Interpretable Semantic Textual Similarity along with the results obtained for our sub- mitted runs. …...

AbstractSemantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features …...

This paper introduces STEM and LAMB, em- beddings trained for stems and lemmata in- stead of for surface forms. For morpholog- ically rich languages, they perform signif- icantly better than …...

We study the problem of automatically building hypernym taxonomies from tex- tual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important …...

AbstractThe last two decades have seen the development of various semantic lexical resources such as WordNet (Miller, 1995) and the USAS semantic lexicon (Rayson et al., 2004), which have played …...

Deep inference on a large-scale knowledge base (KB) needs a mass of formulas, but it is almost impossible to create all formulas man- ually. Data-driven methods have been pro- posed …...

AbstractWe have released plWordNet 3.0, a very large wordnet for Polish. In addition to what is expected in wordnets – richly interrelated synsets – it contains sentiment and emotion annotations, …...

Jauhar et al. (2015) recently proposed to learn sense-specific word representations by “retrofitting” standard distributional word rep- resentations to an existing ontology. We ob- serve that this approach does not …...

Manually constructed taxonomies provide a crucial resource for many NLP technologies, yet these resources are often limited in their lexical coverage due to their construction pro- cedure. While multiple approaches …...

In this paper, a system for semantic tex- tual similarity, which participated in Task- 1 in SemEval 2016 (monolingual and cross- lingual sub-tasks) is described. The system contains a preprocessing …...

AbstractWe present a WordNet like structured resource for slang words and neologisms on the internet. The dynamism of language is often an indication that current language technology tools trained on …...

The SemEval 2016 DiMSUM Shared Task concerns the detection of minimal semantic units from text and prediction of their coarse lexical categories known as supersenses. Our approach is to define …...

VRep is a system designed for SemEval 2016 Task 1 - Semantic Textual Similarity (STS) and Task 2 - Interpretable Semantic Textual Similarity (iSTS). STS quantifies the seman- tic equivalence …...

AbstractThe automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as since or but, are the most informative cues to identify explicit …...

AbstractThis paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ …...

AbstractOur goal is to combine the rich multi-step inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and …...

AbstractThis paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use …...

AbstractWordNet 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, …...

AbstractType-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed …...

AbstractAnnotating 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 …...

AbstractThis paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can …...

AbstractThis paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert …...

AbstractAnimacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, …...

WordNets – lexical databases in which groups of synonyms are arranged according to the semantic relationships between them – are crucial resources in semantically-focused natural language processing tasks, but are …...

AbstractThis paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with …...

In this paper we present a novel approach to the construction of an extensive, sense-level sentiment lexicon built on the basis of a wordnet. The main aim of this work …...

AbstractCluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such …...

We report on the practical application of a black-box testing methodology for the validation of the knowledge encoded in WordNet, SUMO and their mapping by using automated theorem provers. Our …...

AbstractIn this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical …...

Classifying the relationship between entities is an important natural language processing (NLP) task. Scientific Relation Classification aims at automatically categorizing scientific semantic relationships among entities in sci- entific documents. Conventionally, …...

Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing …...

Metaphors are not only remarkably pervasive in both informal and formal text, but reflect fundamental properties of human cognition. This paper presents an algorithmic model that suggests metaphoric means of …...

AbstractWord vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. …...

AbstractThis paper describes CzEngClass, a bilingual lexical resource being built to investigate verbal synonymy in bilingual context and to relate semantic roles common to one synonym class to verb arguments …...

In Word Sense Disambiguation, sense annotated corpora are often essential for evaluating a system and also valuable in order to reach a good efficiency. Always created for a specific purpose, …...

AbstractIn this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, …...

AbstractThis paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants. The system consists of 3 stacked LSTMs: …...

WordNet-like resources are lexical databases with highly relevance information and data which could be exploited in more complex computational linguistics research and applications. The building process requires manual and automatic …...

AbstractThis paper introduces several improvements over the current state of the art in knowledge-based word sense disambiguation. Those innovations are the result of modifying and enriching a knowledge base created …...

AbstractContextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we …...

AbstractWhen reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. …...

AbstractRecent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the …...

AbstractThis paper outlines procedures for enhancing WordNet with conceptual information from FrameNet. The mapping of the two resources is non-trivial. We define a number of techniques for the validation of …...

AbstractWe present the parallel creation of a WordNet resource for Swedish and Bulgarian which is tightly aligned with the Princeton WordNet. The alignment is not only on the synset level, …...

AbstractWe present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of relation inequality constraints …...

AbstractContextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge …...

AbstractDomain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose …...

AbstractThis paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is …...

AbstractWe present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better …...

AbstractCognates are present in multiple variants of the same text across different languages (e.g., “hund” in German and “hound” in the English language mean “dog”). They pose a challenge to …...

AbstractThis is a resource description paper that describes the creation and properties of a set of pseudo-corpora generated artificially from a random walk over the English WordNet taxonomy. Our WordNet …...

AbstractThe sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class …...

AbstractDefinition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works …...

AbstractAnswer selection (AS) is an important subtask of document-based question answering (DQA). In this task, the candidate answers come from the same document, and each answer sentence is semantically related …...

AbstractLanguage models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains …...

AbstractWe formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate …...

AbstractAccurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations. To alleviate the need for labeled data, we introduce WikiNLI: a resource for improving …...

AbstractWe present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category …...

AbstractThe ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision techniques operate at the word form …...

AbstractLexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient. If ambiguous words cannot be disambiguated from context, however, this …...

AbstractWe present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles …...

AbstractState-of-the-art contextual embeddings are obtained from large language models available only for a few languages. For others, we need to learn representations using a multilingual model. There is an ongoing …...

AbstractNeural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been …...

AbstractHumans create things for a reason. Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc. The prototypical function of a physical artifact is a …...

AbstractIn this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021. In this task, systems should determine whether a word that occurs in two different …...

AbstractBasic-level categories (BLC) are an important psycholinguistic concept introduced by Rosch et al. (1976); they are defined as the most inclusive categories for which a concrete mental image of the …...

AbstractIn this paper, we introduce our system that we participated with at the multilingual and cross-lingual word-in-context disambiguation SemEval 2021 shared task. In our experiments, we investigated the possibility of …...

AbstractThe number of senses of a given word, or polysemy, is a very subjective notion, which varies widely across annotators and resources. We propose a novel method to estimate polysemy …...

Many studies were recently done for investi- gating the properties of contextual language models but surprisingly, only a few of them consider the properties of these models in terms of …...

AbstractManifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and …...

AbstractWe propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained lan-guage models. It uses clustering to discover the encoded concepts and explains …...

AbstractAligning lexical resources that associate words with concepts in multiple languages increases the total amount of semantic information that can be leveraged for various NLP tasks. We present a translation-based …...

AbstractLinguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a …...

AbstractOne of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is …...

AbstractWith Web API technology becoming increasingly mature, how to integrate Web API and Chatbot technology has become an issue of great interest. This study plans to build a semi-automatic method …...

AbstractWord sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging …...

AbstractThe psychological plausibility of word embeddings has been studied through different tasks such as word similarity, semantic priming, and lexical entailment. Recent work on predicting category structure with word embeddings …...

AbstractGiven a word in context, the task of VisualWord Sense Disambiguation consists of select-ing the correct image among a set of candidates. To select the correct image, we propose a …...

AbstractThis paper describes our system used in SemEval-2023 Task-1: Visual Word Sense Disambiguation (VWSD). The VWSD task is to identify the correct image that corresponds to an ambiguous target word …...