| dc.creator (作者) | 苑守慈 | zh_TW |
| dc.creator (作者) | Yuan, Soe-Tsyr | - |
| dc.date (日期) | 2004 | en_US |
| dc.date.accessioned | 16-一月-2009 10:55:49 (UTC+8) | - |
| dc.date.available | 16-一月-2009 10:55:49 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-一月-2009 10:55:49 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/26097 | - |
| 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 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. | - |
| dc.format | application/pdf | en_US |
| dc.format.extent | 142403 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | IEEE/WIC International Conference on Web Intelligence | en_US |
| dc.title (題名) | Ontology-Based Structured Cosine Similarity in Speech Document Summarization | en_US |
| dc.type (資料類型) | conference | en |
| dc.identifier.doi (DOI) | 10.1109/WI.2004.110 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1109/WI.2004.110 | - |
