ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding

Cited 46 time in webofscience Cited 52 time in scopus
  • Hit : 279
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorPark, Deokgunko
dc.contributor.authorKim, Seungyeonko
dc.contributor.authorLee, Jurimko
dc.contributor.authorChoo, Jaegulko
dc.contributor.authorDiakopoulos, Nicholasko
dc.contributor.authorElmqvist, Niklasko
dc.date.accessioned2020-03-24T05:20:09Z-
dc.date.available2020-03-24T05:20:09Z-
dc.date.created2020-03-24-
dc.date.created2020-03-24-
dc.date.created2020-03-24-
dc.date.issued2018-01-
dc.identifier.citationIEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.24, no.1, pp.361 - 370-
dc.identifier.issn1077-2626-
dc.identifier.urihttp://hdl.handle.net/10203/273419-
dc.description.abstractCentral to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.titleConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding-
dc.typeArticle-
dc.identifier.wosid000418038400037-
dc.identifier.scopusid2-s2.0-85029172691-
dc.type.rimsART-
dc.citation.volume24-
dc.citation.issue1-
dc.citation.beginningpage361-
dc.citation.endingpage370-
dc.citation.publicationnameIEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS-
dc.identifier.doi10.1109/TVCG.2017.2744478-
dc.contributor.localauthorChoo, Jaegul-
dc.contributor.nonIdAuthorPark, Deokgun-
dc.contributor.nonIdAuthorKim, Seungyeon-
dc.contributor.nonIdAuthorLee, Jurim-
dc.contributor.nonIdAuthorDiakopoulos, Nicholas-
dc.contributor.nonIdAuthorElmqvist, Niklas-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordAuthorText analytics-
dc.subject.keywordAuthorvisual analytics-
dc.subject.keywordAuthorword embedding-
dc.subject.keywordAuthortext summarization-
dc.subject.keywordAuthortext classification-
dc.subject.keywordAuthorconcepts-
dc.subject.keywordPlusNONNEGATIVE MATRIX FACTORIZATION-
dc.subject.keywordPlusDATABASE-
Appears in Collection
AI-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 46 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0