Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김유섭 | * |
dc.date.accessioned | 2017-08-25T03:08:59Z | - |
dc.date.available | 2017-08-25T03:08:59Z | - |
dc.date.issued | 2002 | * |
dc.identifier.isbn | 3540440380 | * |
dc.identifier.isbn | 9783540440383 | * |
dc.identifier.issn | 0302-9743 | * |
dc.identifier.other | OAK-16013 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/235614 | - |
dc.description.abstract | This paper presents an approach to the topic extraction from text documents using probabilistic graphical models. Multiple-cause networks with latent variables are used and the Helmholtz machines are utilized to ease the learning and inference. The learning in this model is conducted in a purely data-driven way and does not require prespecified categories of the given documents. Topic words extraction experiments on the TDT-2collection are presented. Especially, document clustering results on a subset of TREC-8 ad-hoc task data show the substantial reduction of the inference time without significant deterioration of performance. © Springer-Verlag Berlin Heidelberg 2002. | * |
dc.description.sponsorship | Japanese Society for Artificial Intelligence (JSAI) | * |
dc.language | English | * |
dc.publisher | Springer Verlag | * |
dc.title | Topic extraction from text documents using multiple-cause networks | * |
dc.type | Conference Paper | * |
dc.relation.volume | 2417 | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 434 | * |
dc.relation.lastpage | 443 | * |
dc.relation.journaltitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | * |
dc.identifier.scopusid | 2-s2.0-84947942003 | * |
dc.author.google | Chang J.-H. | * |
dc.author.google | Lee J.W. | * |
dc.author.google | Kim Y. | * |
dc.author.google | Zhang B.-T. | * |
dc.date.modifydate | 20240325111531 | * |