A Rapid Algorithm to Chinese Named Entity Recognition Based on Single Character Hints
Dakun Zhang · Zhongwen xinxi xuebao · 2008
Conditional Random Fields(CRF) model becomes prevalent for sequential labeling tasks in the field of NLP.A general but slow optimization algorithm L-BFGS is commonly used in parameter estimation of CRF Model. In this paper,an improved algorithm is proposed to train CRF model more quickly.First,small scale character hint features are introduced to decrease the feature space.Then,a task-specific rule is applied to reduce search paths in Viterbi and Baum-Welch procedure.The experiments on China 863 program NER and SIGHAN 2006 corpora show that our schema saves training time significantly without performance drop.