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題名 Ontology-Based Structured Cosine Similarity in Document Summarization: with Applications to Mobile Audio-Based Knowledge Management
作者 苑守慈;Jerry Sun
Yuan, Soe-Tysr
日期 2005-10
上傳時間 17-Jan-2009 15:59:53 (UTC+8)
摘要 Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of the documents. In this paper, we present a novel method named structured cosine similarity (SCS) that furnishes document clustering with a new way of modeling on document summarization, considering the structure of the documents so as to improve the performance of document clustering in terms of quality, stability, and efficiency. This study was motivated by the problem of clustering speech documents (of no rich document features) attained from the wireless experience oral sharing conducted by mobile workforce of enterprises, fulfilling audio-based knowledge management. In other words, this problem aims to facilitate knowledge acquisition and sharing by speech. The evaluations also show fairly promising results on our method of structured cosine similarity.
關聯 IEEE Transactions on Transactions on Systems Man and Cybernetics Part B, 35(5), 1028-1040
資料類型 article
DOI http://dx.doi.org/10.1109/TSMCB.2005.850153
dc.creator (作者) 苑守慈;Jerry Sunzh_TW
dc.creator (作者) Yuan, Soe-Tysr-
dc.date (日期) 2005-10en_US
dc.date.accessioned 17-Jan-2009 15:59:53 (UTC+8)-
dc.date.available 17-Jan-2009 15:59:53 (UTC+8)-
dc.date.issued (上傳時間) 17-Jan-2009 15:59:53 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/26987-
dc.description.abstract (摘要) Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of the documents. In this paper, we present a novel method named structured cosine similarity (SCS) that furnishes document clustering with a new way of modeling on document summarization, considering the structure of the documents so as to improve the performance of document clustering in terms of quality, stability, and efficiency. This study was motivated by the problem of clustering speech documents (of no rich document features) attained from the wireless experience oral sharing conducted by mobile workforce of enterprises, fulfilling audio-based knowledge management. In other words, this problem aims to facilitate knowledge acquisition and sharing by speech. The evaluations also show fairly promising results on our method of structured cosine similarity.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) IEEE Transactions on Transactions on Systems Man and Cybernetics Part B, 35(5), 1028-1040en_US
dc.title (題名) Ontology-Based Structured Cosine Similarity in Document Summarization: with Applications to Mobile Audio-Based Knowledge Managementen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1109/TSMCB.2005.850153en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/TSMCB.2005.850153en_US