CRF-based Hybrid Model for Word Segmentation, NER and even POS Tagging
نویسندگان
چکیده
This paper presents systems submitted to the close track of Fourth SIGHAN Bakeoff. We built up three systems based on Conditional Random Field for Chinese Word Segmentation, Named Entity Recognition and Part-Of-Speech Tagging respectively. Our systems employed basic features as well as a large number of linguistic features. For segmentation task, we adjusted the BIO tags according to confidence of each character. Our final system achieve a F-score of 94.18 at CTB, 92.86 at NCC, 94.59 at SXU on Segmentation, 85.26 at MSRA on Named Entity Recognition, and 90.65 at PKU on Part-Of-Speech Tagging.
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