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https://ah.lib.nccu.edu.tw/handle/140.119/77997
題名: | A semantic frame-based intelligent agent for topic detection | 作者: | Chang, Yung-Chun;Hsieh, Yu-Lun;Chen, Cen-Chieh;Hsu, Wen-Lian | 貢獻者: | 資訊科學系 | 關鍵詞: | Topic detection;Semantic frame;Semantic class;Partial matching | 日期: | Jan-2017 | 上傳時間: | 27-Aug-2015 | 摘要: | 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. | 關聯: | Soft Computing, Volume 21, Issue 2, pp 391–401 | 資料類型: | article | DOI: | http://dx.doi.org/10.1007/s00500-015-1695-4 |
Appears in Collections: | 期刊論文 |
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