Chinese part-of-speech (POS) tagging assigns one POS tag to each word in a Chinese sentence. However, since words are not demarcated in a Chinese sentence, Chinese POS tagging requires word segmentation as a prerequisite. We could perform Chinese POS tagging strictly after word segmentation (one-at-a-time approach), or perform both word segmentation and POS tagging in a combined, single step simultaneously (all-atonce approach). Also, we could choose to assign POS tags on a word-by-word basis, making use of word features in the surrounding context (word-based), or on a character-by-character basis with character features (character-based). This paper presents an in-depth study on such issues of processing architecture and feature representation for Chinese POS tagging, within a maximum entropy framework. We found that while the all-at-once, characterbased approach is the best, the one-at-a-time, character-based approach is a worthwhile compromise, performing only slightly worse in terms of accuracy, but taking shorter time to train and run. As part of our investigation, we also built a state-of-the-art Chinese word segmenter, which outperforms the best SIGHAN 2003 word segmenters in the closed track on 3 out of 4 test corpora.
Hwee Tou Ng et al. REF investigated word-based and character-based approaches and proposed a maximum entropy character-based POS analyzer.
Chinese Part-Of-Speech Tagging: One-At-A-Time Or All-At-Once? Word-Based Or Character-Based?
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science