MSC+: Language pattern learning for word sense induction and disambiguation

Identifying the correct meaning of words in context or discovering new word senses is particularly useful for several tasks such as question answering, information extraction, information retrieval, and text summarization. However, specially in the context of user-generated contents and on-line comm...

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Detalles Bibliográficos
Autores: Bif Goularte, Fábio, Sorato, Danielly, Modesto Nassar, Silvia, Fileto, Renato, Saggion, Horacio
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/46030
Acceso en línea:http://hdl.handle.net/10230/46030
http://dx.doi.org/10.1016/j.knosys.2019.105017
Access Level:acceso abierto
Palabra clave:Lexical semantics
Information extraction
Linguistic pattern mining
Word sense induction
Word sense disambiguation
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spelling MSC+: Language pattern learning for word sense induction and disambiguationBif Goularte, FábioSorato, DaniellyModesto Nassar, SilviaFileto, RenatoSaggion, HoracioLexical semanticsInformation extractionLinguistic pattern miningWord sense inductionWord sense disambiguationIdentifying the correct meaning of words in context or discovering new word senses is particularly useful for several tasks such as question answering, information extraction, information retrieval, and text summarization. However, specially in the context of user-generated contents and on-line communication (e.g. Twitter), new meanings are continuously crafted by speakers as the result of existing words being used in novel contexts. Consequently, lexical semantics inventories and systems have difficulties to cope with semantic drifting problems. In this work, we propose an approach to induce and disambiguate word senses of some target words in collections of short texts, such as tweets, through the use of fuzzy lexico-semantic patterns that we define as sequences of Morpho-semantic Components (MSC+). We learn these patterns, that we call patterns, from text data automatically. Experimental results show that instances of some patterns arise in a number of tweets, but sometimes using different words to convey the sense of the respective MSC+ in some tweets where pattern instances appear. The exploitation of MSC+ patterns when they induce semantics on target words enable effective word sense disambiguation mechanisms leading to improvements in the state of the art.This work was conducted during a doctorate partially supported by grants of CAPES (Brazilian Coordination of Superior Level Staff Improvement) a research support agency from the Ministry of Education of Brazil. CAPES also supported an internship for international cooperation with the TALN (Natural Language Processing Research Group) at the Pompeu Fabra University in Barcelona, Spain. The last author acknowledges support from the Spanish Government under the María de Maeztu Units of Excellence Programme (MDM-2015-0502).Elsevier20202019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/46030http://dx.doi.org/10.1016/j.knosys.2019.105017reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésKnowledge-Based Systems. 2020;188:105017.© Elsevier http://dx.doi.org/10.1016/j.knosys.2019.105017info:eu-repo/semantics/openAccessoai:recercat.cat:10230/460302026-05-29T05:05:01Z
dc.title.none.fl_str_mv MSC+: Language pattern learning for word sense induction and disambiguation
title MSC+: Language pattern learning for word sense induction and disambiguation
spellingShingle MSC+: Language pattern learning for word sense induction and disambiguation
Bif Goularte, Fábio
Lexical semantics
Information extraction
Linguistic pattern mining
Word sense induction
Word sense disambiguation
title_short MSC+: Language pattern learning for word sense induction and disambiguation
title_full MSC+: Language pattern learning for word sense induction and disambiguation
title_fullStr MSC+: Language pattern learning for word sense induction and disambiguation
title_full_unstemmed MSC+: Language pattern learning for word sense induction and disambiguation
title_sort MSC+: Language pattern learning for word sense induction and disambiguation
dc.creator.none.fl_str_mv Bif Goularte, Fábio
Sorato, Danielly
Modesto Nassar, Silvia
Fileto, Renato
Saggion, Horacio
author Bif Goularte, Fábio
author_facet Bif Goularte, Fábio
Sorato, Danielly
Modesto Nassar, Silvia
Fileto, Renato
Saggion, Horacio
author_role author
author2 Sorato, Danielly
Modesto Nassar, Silvia
Fileto, Renato
Saggion, Horacio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Lexical semantics
Information extraction
Linguistic pattern mining
Word sense induction
Word sense disambiguation
topic Lexical semantics
Information extraction
Linguistic pattern mining
Word sense induction
Word sense disambiguation
description Identifying the correct meaning of words in context or discovering new word senses is particularly useful for several tasks such as question answering, information extraction, information retrieval, and text summarization. However, specially in the context of user-generated contents and on-line communication (e.g. Twitter), new meanings are continuously crafted by speakers as the result of existing words being used in novel contexts. Consequently, lexical semantics inventories and systems have difficulties to cope with semantic drifting problems. In this work, we propose an approach to induce and disambiguate word senses of some target words in collections of short texts, such as tweets, through the use of fuzzy lexico-semantic patterns that we define as sequences of Morpho-semantic Components (MSC+). We learn these patterns, that we call patterns, from text data automatically. Experimental results show that instances of some patterns arise in a number of tweets, but sometimes using different words to convey the sense of the respective MSC+ in some tweets where pattern instances appear. The exploitation of MSC+ patterns when they induce semantics on target words enable effective word sense disambiguation mechanisms leading to improvements in the state of the art.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/46030
http://dx.doi.org/10.1016/j.knosys.2019.105017
url http://hdl.handle.net/10230/46030
http://dx.doi.org/10.1016/j.knosys.2019.105017
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Knowledge-Based Systems. 2020;188:105017.
dc.rights.none.fl_str_mv © Elsevier http://dx.doi.org/10.1016/j.knosys.2019.105017
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Elsevier http://dx.doi.org/10.1016/j.knosys.2019.105017
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
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