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題名 Semantic frame-based natural language understanding for intelligent topic detection agent
作者 Chang, Y.-C.;Hsieh, Y.-L.;Chen, Cen Chieh;Hsu, W.-L.
貢獻者 資科系
關鍵詞 Intelligent systems; Syntactics; Partial matching; Semantic class; Semantic frames; Sequence alignments; Topic detection; Semantics
日期 2014-06
上傳時間 15-Jun-2015 16:05:30 (UTC+8)
摘要 Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we proposed a semantic frame-based method for topic detection that simulates such process in human perception. We took advantage of multiple knowledge sources and identified discriminative patterns from documents through frame generation and matching mechanisms. Results demonstrated 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. Moreover, it also outperforms well-known topic detection methods. © 2014 Springer International Publishing Switzerland.
關聯 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 8481 LNAI, Issue PART 1, 2014, Pages 339-348, 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014; Kaohsiung; Taiwan; 3 June 2014 到 6 June 2014; 代碼 107164
資料類型 conference
DOI http://dx.doi.org/10.1007/978-3-319-07455-9-36
dc.contributor 資科系
dc.creator (作者) Chang, Y.-C.;Hsieh, Y.-L.;Chen, Cen Chieh;Hsu, W.-L.
dc.date (日期) 2014-06
dc.date.accessioned 15-Jun-2015 16:05:30 (UTC+8)-
dc.date.available 15-Jun-2015 16:05:30 (UTC+8)-
dc.date.issued (上傳時間) 15-Jun-2015 16:05:30 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75793-
dc.description.abstract (摘要) Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we proposed a semantic frame-based method for topic detection that simulates such process in human perception. We took advantage of multiple knowledge sources and identified discriminative patterns from documents through frame generation and matching mechanisms. Results demonstrated 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. Moreover, it also outperforms well-known topic detection methods. © 2014 Springer International Publishing Switzerland.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 8481 LNAI, Issue PART 1, 2014, Pages 339-348, 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014; Kaohsiung; Taiwan; 3 June 2014 到 6 June 2014; 代碼 107164
dc.subject (關鍵詞) Intelligent systems; Syntactics; Partial matching; Semantic class; Semantic frames; Sequence alignments; Topic detection; Semantics
dc.title (題名) Semantic frame-based natural language understanding for intelligent topic detection agent
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1007/978-3-319-07455-9-36
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-319-07455-9-36