Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/26097


Title: Ontology-Based Structured Cosine Similarity in Speech Document Summarization
Authors: 苑守慈
Yuan, Soe-Tsyr
Date: 2004
Issue Date: 2009-01-16 10:55:49 (UTC+8)
Abstract: 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 terms in documents. In this paper we present a novel method named Structured Cosine Similarity that furnishes document clustering with a new way of modeling on document summarization, considering the structure of terms in documents in order to improve the quality of speech document clustering.
Relation: IEEE/WIC International Conference on Web Intelligence
Data Type: conference
DOI 連結: http://dx.doi.org/10.1109/WI.2004.110
Appears in Collections:[資訊管理學系] 會議論文

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