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3 results on '"Sense similarity"'

1. An overview of word and sense similarity.

Author

Navigli, Roberto and Martelli, Federico

Subjects
NATURAL language processing, SEMANTIC computing, RESEMBLANCE (Philosophy)
Abstract

Over the last two decades, determining the similarity between words as well as between their meanings, that is, word senses, has been proven to be of vital importance in the field of Natural Language Processing. This paper provides the reader with an introduction to the tasks of computing word and sense similarity. These consist in computing the degree of semantic likeness between words and senses, respectively. First, we distinguish between two major approaches: the knowledge-based approaches and the distributional approaches. Second, we detail the representations and measures employed for computing similarity. We then illustrate the evaluation settings available in the literature and, finally, discuss suggestions for future research. [ABSTRACT FROM AUTHOR]

Published
2019
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2. An overview of word and sense similarity

Author

Roberto Navigli and Federico Martelli

Subjects
semantic similarity, Linguistics and Language, Computer science, 02 engineering and technology, computer.software_genre, Language and Linguistics, Field (computer science), strategies, tools, standards for lexicographic resources (objective 3), Semantic similarity, Artificial Intelligence, 020204 information systems, Similarity (psychology), 0202 electrical engineering, electronic engineering, information engineering, business.industry, sense similarity, knowledge-based similarity, word similarity, distributional semantics, WP3, 020201 artificial intelligence & image processing, Distributional semantics, Artificial intelligence, business, computer, Software, Word (computer architecture), Natural language processing
Abstract

Over the last two decades, determining the similarity between words as well as between their meanings, that is, word senses, has been proven to be of vital importance in the field of Natural Language Processing. This paper provides the reader with an introduction to the tasks of computing word and sense similarity. These consist in computing the degree of semantic likeness between words and senses, respectively. First, we distinguish between two major approaches: the knowledge-based approaches and the distributional approaches. Second, we detail the representations and measures employed for computing similarity. We then illustrate the evaluation settings available in the literature and, finally, discuss suggestions for future research.

Published
2019

3. Aligning an Italian WordNet with a lexicographic dictionary: Coping with limited data

Author

Tommaso Caselli, Carlo Strapparava, Laure Vieu, Guido Vetere, Trento Rise (ITALY), Fondazione Bruno Kessler [Trento, Italy] (FBK), MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Laboratory for Applied Ontology (LOA), Istituto di Scienze e Tecnologie della Cognizione [Trento] (ISTC-CNR), Centre National de la Recherche Scientifique (CNRS), IBM Centers for Advanced Studies - CAS (ITALY), Centre National de la Recherche Scientifique - CNRS (FRANCE), Consiglio Nazionale delle Ricerche - CNR (ITALY), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Fondazione Bruno Kessler - FBK (ITALY), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Logique en informatique, Sense similarity, Lexical matching, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Senso commune, Multiwordnet, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Informatique et langage, Intelligence artificielle, Apprentissage, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; This work describes the evaluations of two approaches, Lexical Matching and Sense Similarity, for word sense alignment between MultiWordNet and a lexicographic dictionary, Senso Comune De Mauro, when having few sense descriptions (MultiWordNet) and no structure over senses (Senso Comune De Mauro). The results obtained from the merging of the two approaches are satisfying, with F1 values of 0.47 for verbs and 0.64 for nouns.

Published
2014

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