Abstract
In Chinese language processing, new words are particularly problematic. It is impossible to get a complete dictionary as new words can always be created. We proposed a unified dual-chain unequal-state CRF model to detect new words together with their part-of-speech in Chinese texts regardless of the word types such as compound words, abbreviation, person names, etc. The dual-chain unequal-state CRF model has two state chains with unequal number of states. The unequal state chains could model flexible hierarchical lexical information for both Chinese new word detection and POS tagging, and also integrate complex context features like the global information. The experimental results show that the proposed method is capable of detecting even low frequency new words and their parts-of-speech synchronously with satisfactory results.
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