ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Deokgun | - |
dc.contributor.author | Kim, Seungyeon | - |
dc.contributor.author | Lee, Jurim | - |
dc.contributor.author | Choo, Jaegul | - |
dc.contributor.author | Diakopoulos, Nicholas | - |
dc.contributor.author | Elmqvist, Niklas | - |
dc.date.accessioned | 2021-09-02T16:59:01Z | - |
dc.date.available | 2021-09-02T16:59:01Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 1077-2626 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/78416 | - |
dc.description.abstract | Central 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.subject | NONNEGATIVE MATRIX FACTORIZATION | - |
dc.subject | DATABASE | - |
dc.title | ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choo, Jaegul | - |
dc.identifier.doi | 10.1109/TVCG.2017.2744478 | - |
dc.identifier.scopusid | 2-s2.0-85029172691 | - |
dc.identifier.wosid | 000418038400037 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.24, no.1, pp.361 - 370 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | - |
dc.citation.title | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | - |
dc.citation.volume | 24 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 361 | - |
dc.citation.endPage | 370 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | NONNEGATIVE MATRIX FACTORIZATION | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordAuthor | Text analytics | - |
dc.subject.keywordAuthor | visual analytics | - |
dc.subject.keywordAuthor | word embedding | - |
dc.subject.keywordAuthor | text summarization | - |
dc.subject.keywordAuthor | text classification | - |
dc.subject.keywordAuthor | concepts | - |
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