Abstract
The performance of Chinese Pinyin-to-Character conversion is severely affected when the characteristics of the training and conversion data differ. As natural language is highly variable and uncertain, it is impossible to build a complete and general language model to suit all the tasks. The traditional adaptive MAP models mix the task independent data with task dependent data using a mixture coefficient but we never can predict what style of language users have and what new domain will appear. This paper presents a statistical error-driven adaptive language modeling approach to Chinese Pinyin input system. This model can be incrementally adapted when an error occurs during Pinyin-to-Character converting time. It significantly improves Pinyin-to-Character conversion rate.
Original language | English |
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Pages | 19-22 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Event | International Conference on Asian Language Processing - Duration: 15 Nov 2011 → … |
Conference
Conference | International Conference on Asian Language Processing |
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Period | 15/11/11 → … |
Keywords
- Adaptive Learning
- Chinese Language Processing
- Pinyin-to-Character Conversion
- Statistical Language Modeling