Classifying Travel-related Intents in Textual Data

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dc.contributor.authorKim, Zae Myungko
dc.contributor.authorJeong, Young-Seobko
dc.contributor.authorHyeon, Jonghwanko
dc.contributor.authorOh, Hyngraiko
dc.contributor.authorChoi, Ho-Jinko
dc.date.accessioned2016-12-01T06:58:20Z-
dc.date.available2016-12-01T06:58:20Z-
dc.date.created2016-06-10-
dc.date.created2016-06-10-
dc.date.issued2016-01-
dc.identifier.citationInternational Journal of Computing, Communication and Instrumentation Engineering, v.3, no.1, pp.96 - 101-
dc.identifier.issn2349-1469-
dc.identifier.urihttp://hdl.handle.net/10203/214545-
dc.description.abstractIntent classification refers to the process of identifying a set of intents of interest that appear in a given document. This work considers the task of annotating travel-related reviews with travel intents that best represent the reviewer's reason for visiting the place of interest (POI). A domain-tailored word embedding model is learned to construct intent-specific feature vectors, thereby improving classification accuracy. The feasibility of multiclass intent classification is explored using an intent corpus, consisting of 6,560 labelled reviews.-
dc.languageEnglish-
dc.publisherInternational Institute of Engineers-
dc.titleClassifying Travel-related Intents in Textual Data-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume3-
dc.citation.issue1-
dc.citation.beginningpage96-
dc.citation.endingpage101-
dc.citation.publicationnameInternational Journal of Computing, Communication and Instrumentation Engineering-
dc.identifier.doi10.15242/IJCCIE.ER01161004-
dc.contributor.localauthorChoi, Ho-Jin-
dc.contributor.nonIdAuthorOh, Hyngrai-
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