download74 view233
twitter facebook

CC_BYThis item is licensed Creative Commons License

dc.contributor.author
Heung-Seon Oh
dc.contributor.author
Yuchul Jung
dc.date.accessioned
2018-10-12T04:51:18Z
dc.date.available
2018-10-12T04:51:18Z
dc.date.issued
2017-09-30
dc.identifier.issn
2287-4577
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/8688
dc.description.abstract
The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.
dc.format
application/pdf
dc.language.iso
eng
dc.relation.ispartofseries
Journal of Information Science Theory and Practice
dc.title
Enhancing the Narrow-down Approach to Large-scale Hierarchical Text Classification with Category Path Information
dc.type
Article
dc.rights.license
CC_BY
dc.citation.endPage
47
dc.citation.number
3
dc.citation.startPage
31
dc.citation.volume
5
dc.identifier.bibliographicCitation
vol. 5, no. 3, page. 31 - 47
dc.identifier.doi
10.1633/JISTaP.2017.5.3.3
dc.subject.keyword
Hierarchical text classification
dc.subject.keyword
Query expansion
dc.subject.keyword
Narrow-down approach
dc.rights.holder
KISTI
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 5 - No. 3
Files in This Item:
Thumbnail E1JSCH_2017_v5n3_31.pdf812.41 kBDownload

Browse