Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/77997
DC Field | Value | Language |
---|---|---|
dc.contributor | 資訊科學系 | - |
dc.creator | Chang, Yung-Chun;Hsieh, Yu-Lun;Chen, Cen-Chieh;Hsu, Wen-Lian | - |
dc.date | 2017-01 | - |
dc.date.accessioned | 2015-08-27T09:17:22Z | - |
dc.date.available | 2015-08-27T09:17:22Z | - |
dc.date.issued | 2015-08-27T09:17:22Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/77997 | - |
dc.description.abstract | Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we propose a semantic frame-based topic detection (SFTD) that simulates such process in human perception. We take advantage of multiple knowledge sources and extracted discriminative patterns from documents through a highly automated, knowledge-supported frame generation and matching mechanisms. Using a Chinese news corpus containing over 111,000 news articles, we provide a comprehensive performance evaluation which demonstrates that our novel approach can effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Experimental results show that SFTD is comparable to other well-known topic detection methods. | - |
dc.format.extent | 4801735 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.relation | Soft Computing, Volume 21, Issue 2, pp 391–401 | - |
dc.subject | Topic detection;Semantic frame;Semantic class;Partial matching | - |
dc.title | A semantic frame-based intelligent agent for topic detection | - |
dc.type | article | en |
dc.identifier.doi | 10.1007/s00500-015-1695-4 | - |
dc.doi.uri | http://dx.doi.org/10.1007/s00500-015-1695-4 | - |
item.openairetype | article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | restricted | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | 期刊論文 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
s00500-015-1695-4.pdf | 4.69 MB | Adobe PDF2 | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.