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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Song Chunyu Kit Ruifeng Xu Hai Zhao |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Chinese, Translation and Linguistics, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong (Yan Song; Chunyu Kit; Ruifeng Xu; Hai Zhao) |
| Abstract | Integrating global information of unsupervised segmentation into Conditional Random Fields (CRF) learning has been proved effective to enhance the performance of the character tagging based Chinese Word Segmentation. By comparing CRF models with and without unsupervised learning enhancement, we investigate how unsupervised learning affects the performance. Especially, two kinds of segmented words, in-vocabulary and out-of-vocabulary words, are separately analyzed case by case to see what part of those words are affected by unsupervised learning. In addition, the cost of the additional features derived from unsupervised segmentation are also taken into account and evaluated. |
| Starting Page | 3481 |
| Ending Page | 3486 |
| File Size | 368826 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424437023 |
| DOI | 10.1109/ICMLC.2009.5212769 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Unsupervised learning Tagging Machine learning Cybernetics frequent substring extraction Chinese word segmentation in-vocabulary words out-of-vocabulary words |
| Content Type | Text |
| Resource Type | Article |
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