Which Sentence Representation is More Informative: An Analysis on Text Classification

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dc.contributor.authorBölücü, Necva
dc.contributor.authorCan, Burcu
dc.date.accessioned2025-01-06T17:29:55Z
dc.date.available2025-01-06T17:29:55Z
dc.date.issued2023
dc.descriptionGeorgetown College of Arts and Sciences; Georgetown Department of Linguistics; Georgetown Faculty of Languages and Linguistics
dc.description7th International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023), Depling 2023 -- 9 March 2023 through 12 March 2023 -- Virtual, Online -- 192806
dc.description.abstractText classification is a popular and well-studied problem in Natural Language Processing. Most previous work on text classification has focused on deep neural networks such as LSTMs and CNNs. However, text classification studies using syntactic and semantic information are very limited in the literature. In this study, we propose a model using Graph Attention Network (GAT) that incorporates semantic and syntactic information as input for the text classification task. The semantic representations of UCCA and AMR are used as semantic information and the dependency tree is used as syntactic information. Extensive experimental results and in-depth analysis show that UCCA-GAT model, which is a semantic-aware model outperforms the AMR-GAT and DEP-GAT, which are semantic and syntax-aware models respectively. We also provide a comprehensive analysis of the proposed model to understand the limitations of the representations for the problem. © 2023 Association for Computational Linguistics.
dc.identifier.endpage21
dc.identifier.isbn978-195942932-6
dc.identifier.scopus2-s2.0-85175403536
dc.identifier.startpage9
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1388
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAssociation for Computational Linguistics
dc.relation.ispartofDepling 2023 - 7th International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023), Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectClassification (of information)
dc.subjectComputational linguistics
dc.subjectDeep neural networks
dc.subjectNatural language processing systems
dc.subjectSyntactics
dc.subjectText processing
dc.subjectClassification tasks
dc.subjectDependency trees
dc.subjectIn-depth analysis
dc.subjectLanguage processing
dc.subjectNatural languages
dc.subjectNetwork models
dc.subjectSemantic representation
dc.subjectSemantics Information
dc.subjectSyntactic information
dc.subjectText classification
dc.subjectSemantics
dc.titleWhich Sentence Representation is More Informative: An Analysis on Text Classification
dc.typeConference Object

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