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The paper addresses the problem of automatic enrichment of a thesaurus by classifying new words into its classes. The proposed classification method makes use of both the distributional data about …...

Semantic relatedness is a very important fac- tor for the coreference resolution task. To obtain this semantic information, corpus- based approaches commonly leverage pat- terns that can express a specific …...

Semantic relatedness between words is important to many NLP tasks, and nu- merous measures exist which use a vari- ety of resources. Thus far, such work is confined to measuring …...

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 …...

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 …...

A range of Natural Language Process- ing tasks involve making judgments about the semantic relatedness of a pair of sen- tences, such as Recognizing Textual En- tailment (RTE) and answer …...

. Semantic lexical resources are a mainstay of various NLP applications. However, comprehensive and reliable resources rarely exist or are often not freely available. We discuss in this paper the …...

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 …...

AbstractCollocations can be defined as words that occur together significantly more often than it would be expected by chance. Many natural language processing applications such as natural language generation, word …...

This paper explores the hypothesis that se- mantic relatedness may be more reliably in- ferred by using a multilingual space, as com- pared to the typical monolingual representa- tion. Through …...

The measurement of semantic relatedness between two words is an important met- ric for many natural language processing applications. In this paper, we present a novel approach for measuring semantic …...

The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised ver- sion based on two classification …...

This paper presents our approach to se- mantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two ques- tions: (1) Can we solve …...

Explicit Semantic Analysis (ESA) is an ap- proach to calculate the semantic relatedness between two words or natural language texts with the help of concepts grounded in human cognition. ESA …...

This paper reports the description and scores of our system, FBK-TR, which participated at the SemEval 2014 task #1 "Evaluation of Compositional Distribu- tional Semantic Models on Full Sentences through …...

This paper presents the task on the evalu- ation of Compositional Distributional Se- mantics Models on full sentences orga- nized for the first time within SemEval- 2014. Participation was open …...

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod- els of vector-space representations. Yet while ‘multi-sense’ methods have been proposed …...

AbstractIn this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, …...

AbstractTarget-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment …...

AbstractEmotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness. Most emotion lexicons contain a …...

Segmenting text into semantically coher- ent fragments improves readability of text and facilitates tasks like text summariza- tion and passage retrieval. In this pa- per, we present a novel unsupervised …...

AbstractRecognizing and generating paraphrases is an important component in many natural language processing applications. A well-established technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a …...

AbstractWith the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To …...

AbstractApplications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based …...

AbstractEffective representation of biomedical names for downstream NLP tasks requires the encoding of both lexical as well as domain-specific semantic information. Ideally, the synonymy and semantic relatedness of names should …...

AbstractRelation schemas are often pre-defined for each relation dataset. Relation types can be related from different datasets and have overlapping semantics. We hypothesize we can combine these datasets according to …...

AbstractIn this paper, we introduce the task of learning unsupervised dialogue embeddings.Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been …...