DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hua, Ting | ko |
dc.contributor.author | Lu, Chang-Tien | ko |
dc.contributor.author | Choo, Jaegul | ko |
dc.contributor.author | Reddy, Chandan K. | ko |
dc.date.accessioned | 2021-01-04T09:10:14Z | - |
dc.date.available | 2021-01-04T09:10:14Z | - |
dc.date.created | 2020-12-03 | - |
dc.date.created | 2020-12-03 | - |
dc.date.created | 2020-12-03 | - |
dc.date.created | 2020-12-03 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.citation | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v.14, no.2, pp.24 | - |
dc.identifier.issn | 1556-4681 | - |
dc.identifier.uri | http://hdl.handle.net/10203/279465 | - |
dc.description.abstract | Probabilistic topic models, which can discover hidden patterns in documents, have been extensively studied. However, rather than learning from a single document collection, numerous real-world applications demand a comprehensive understanding of the relationships among various document sets. To address such needs, this article proposes a new model that can identify the common and discriminative aspects of multiple datasets. Specifically, our proposed method is a Bayesian approach that represents each document as a combination of common topics (shared across all document sets) and distinctive topics (distributions over words that are exclusive to a particular dataset). Through extensive experiments, we demonstrate the effectiveness of our method compared with state-of-the-art models. The proposedmodel can be useful for "comparative thinking" analysis in real-world document collections. | - |
dc.language | English | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | Probabilistic Topic Modeling for Comparative Analysis of Document Collections | - |
dc.type | Article | - |
dc.identifier.wosid | 000537966100004 | - |
dc.identifier.scopusid | 2-s2.0-85081627935 | - |
dc.type.rims | ART | - |
dc.citation.volume | 14 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 24 | - |
dc.citation.publicationname | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA | - |
dc.identifier.doi | 10.1145/3369873 | - |
dc.contributor.localauthor | Choo, Jaegul | - |
dc.contributor.nonIdAuthor | Hua, Ting | - |
dc.contributor.nonIdAuthor | Lu, Chang-Tien | - |
dc.contributor.nonIdAuthor | Reddy, Chandan K. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Probabilistic topic modeling | - |
dc.subject.keywordAuthor | text mining | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.