Towards a hybrid model for Chinese word segmentation

This paper describes a hybrid Chinese word segmenter that is being developed as part of a larger Chinese unknown word resolution system. The segmenter consists of two components: a tagging component that uses the transformation-based learning algorithm to tag each character with its position in a word, and a merging component that transforms a tagged character sequence into a word-segmented sentence. In addition to the position-of-character tags assigned to the characters, the merging component makes use of a number of heuristics to handle non-Chinese characters, numeric type compounds, and long words. The segmenter achieved a 92.8% F-score and a 72.8% recall for OOV words in the closed track of the Peking University Corpus in the Second International Chinese Word Segmentation Bakeoff.