정보과학회논문지 (Journal of KIISE)
Current Result Document :
| 한글제목(Korean Title) |
LDA와 WMD 기반의 공간 변환을 이용한 효과적인 문서 클러스터링 방법 |
| 영문제목(English Title) |
An Efficient Document Clustering Method using Space Transformation based on LDA and WMD |
| 저자(Author) |
김용담
정성원
Yongdam Kim
Sungwon Jung
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| 원문수록처(Citation) |
VOL 48 NO. 09 PP. 1052 ~ 1060 (2021. 09) |
한글내용
(Korean Abstract) |
기존의 TF-IDF 기반의 문서 클러스터링 기법은 문서의 문맥 정보인 co-occurrence와 wordorder에 대한 정보를 충분히 활용하지 못하고, |
영문내용
(English Abstract) |
The existing TF-IDF-based document clustering methods do not properly exploit the contextual information of documents, i.e., co-occurence and word-order, and tend to degrade the performance due to the curse of dimensionality. To overcome these problems, the techniques such as a weighted average of word embedding vectors or Word Mover's Distance (WMD) have been proposed. The performance of the techniques is good at document classification, but not a document clustering that needs to group documents. In this study, we define a document group as a topic document using LDA, the document group's representative document, and solve the existing problem by calculating the WMD based on the topic document. However, since WMD requires a large amount of computation, we propose a space transformation method that shows a good performance while reducing the computation cost by mapping each document to a low-dimensional space in which each axis means WMD value from each topic document. |
| 키워드(Keyword) |
문서 클러스터링
word mover's distance
공간 변환 기법
워드 임베딩
document clustering
word mover's distance
space
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