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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chenda Nou Kameyama, W. |
| Copyright Year | 2007 |
| Description | Author affiliation: Waseda Univ., Honjo (Chenda Nou; Kameyama, W.) |
| Abstract | New words are being created everyday and the lexicon is not large enough to cover all the words, unknown words become a serious problem in part-of-speech tagging. This paper presents a hybrid approach to handle the unknown word problem in Khmer part-of-speech tagging. The hybrid approach combined of rule-based model and trigram model makes use of both internal structure of the word and surrounding contextual information to predict the part-of-speech of unknown words. The proposed approach achieves 88.9% and 78.2% of accuracy on training and test data respectively. |
| Starting Page | 215 |
| Ending Page | 220 |
| File Size | 433561 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424414997 |
| DOI | 10.1109/IRI.2007.4296623 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tagging Natural languages Context modeling Predictive models Testing Entropy Machine learning Data mining Decision trees Neural networks |
| Content Type | Text |
| Resource Type | Article |
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