完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.author | Liang, Tyne | |
dc.contributor.author | Shih, Ping-Ke | |
dc.contributor.author | Wu, Diang-Song | |
dc.date.accessioned | 2009-06-02T06:40:28Z | |
dc.date.accessioned | 2020-05-25T06:42:18Z | - |
dc.date.available | 2009-06-02T06:40:28Z | |
dc.date.available | 2020-05-25T06:42:18Z | - |
dc.date.issued | 2006-10-12T08:00:27Z | |
dc.date.submitted | 2004-12-15 | |
dc.identifier.uri | http://dspace.lib.fcu.edu.tw/handle/2377/1085 | - |
dc.description.abstract | Named Entity Recognition (NER) is one of essential tasks for knowledge acquisition from scientific literature. In this paper, a full automatic named entities recognition from biomedical literature is presented by using Hidden Markov Model in which a rich set of features are concerned and back-off strategy is employed to overcome data sparseness problem. Experiments with GENIA corpora of different versions showed that the presented approach achieved promising results of 76% and 62% F-score for singular-type and multiple-type entities recognition respectively. | |
dc.description.sponsorship | 大同大學,台北市 | |
dc.format.extent | 6p. | |
dc.format.extent | 306666 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | zh_TW | |
dc.relation.ispartofseries | 2004 ICS會議 | |
dc.subject.other | Bioinformatics | |
dc.title | Statistical Approaches to Biomedical Entities Recognition | |
分類: | 2004年 ICS 國際計算機會議 |
文件中的檔案:
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ce07ics002004000205.pdf | 299.48 kB | Adobe PDF | 檢視/開啟 |
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