An integrated approach to automatic synonym detection in Turkish corpus

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dc.authorscopusid34978067500
dc.authorscopusid23096618000
dc.authorscopusid22978771800
dc.contributor.authorYıldız, T.
dc.contributor.authorYıldırım, S.
dc.contributor.authorDiri, B.
dc.date.accessioned2024-07-18T20:16:45Z
dc.date.available2024-07-18T20:16:45Z
dc.date.issued2014
dc.description.abstractIn this study, we designed a model to determine synonymy. Our main assumption is that synonym pairs show similar semantic and dependency relation by the definition. They share same meronym/holonym and hypernym/hyponym relations. Contrary to synonymy, hypernymy and meronymy relations can probably be acquired by applying lexico-syntactic patterns to a big corpus. Such acquisition might be utilized and ease detection of synonymy. Likewise, we utilized some particular dependency relations such as object/subject of a verb, etc. Machine learning algorithms were applied on all these acquired features. The first aim is to find out which dependency and semantic features are the most informative and contribute most to the model. Performance of each feature is individually evaluated with cross validation. The model that combines all features shows promising results and successfully detects synonymy relation. The main contribution of the study is to integrate both semantic and dependency relation within distributional aspect. Second contribution is considered as being first major attempt for Turkish synonym identification based on corpus-driven approach. © Springer International Publishing Switzerland 2014.en_US
dc.identifier.doi10.1007/978-3-319-10888-9_12
dc.identifier.endpage127en_US
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84921633560en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage116en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-319-10888-9_12
dc.identifier.urihttps://hdl.handle.net/11411/6240
dc.identifier.volume8686en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDependency Relationsen_US
dc.subjectNear-Synonymen_US
dc.subjectPattern-Baseden_US
dc.subjectSynonymen_US
dc.subjectLearning Algorithmsen_US
dc.subjectSemanticsen_US
dc.subjectLearning Systemsen_US
dc.subjectLinguisticsen_US
dc.subjectNatural Language Processing Systemsen_US
dc.subjectCross Validationen_US
dc.subjectDependency Relationen_US
dc.subjectIntegrated Approachen_US
dc.subjectLexico-Syntactic Patternsen_US
dc.subjectNear-Synonymen_US
dc.subjectPattern-Baseden_US
dc.subjectSemantic Featuresen_US
dc.subjectSynonymen_US
dc.subjectMachine Learningen_US
dc.subjectSemanticsen_US
dc.titleAn integrated approach to automatic synonym detection in Turkish corpusen_US
dc.typeArticleen_US

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