This paper describes a hybrid model that combines machine learning with linguistic heuristics for integrating unknown word identification with Chinese word segmentation. The model consists of two components: a position-of-character (POC) tagging component that annotates each character in a sentence with a POC tag that indicates its position in a word, and a merging component that transforms a POC-tagged character sequence into a word-segmented sentence. The tagging component uses a support vector machine based tagger to produce an initial tagging of the text and a transformation-based tagger to improve the initial tagging. In addition to the POC tags assigned to the characters, the merging component incorporates a number of linguistic and statistical heuristics to detect words with regular internal structures, recognize long words, and filter non-words. Experiments show that, without resorting to a separate unknown word identification mechanism, the model achieves an F-score of 95.0% for word segmentation and a competitive recall of 74.8% for unknown word recognition.
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