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• [P734] CRF-based Active Learning for Chinese Named Entity Recognition
  Author(s): Lin Yao, Chengjie Sun, Shaofeng Li, Xiaolong Wang, Xuan Wang
  
  

  Abstract
  Conditional Random Fields (CRFs) have been used for many sequence labeling tasks and got excellent results. However this supervised model strongly depends on the huge training data. Active learning is a different way rather than relying on a large amount random sampling, but constructively participates in the optimal choosing training examples. Based on different query strategies, active learning can combine with other machine learning methods to reduce annotation cost while maintaining accuracy. This paper proposes a new active learning strategy based on Information Density (ID) integrated with CRFs for Chinese Named Entity Recognition (NER). On Sighan bakeoff 2006 MSRA NER corpus, an F1 score of 77.2% is achieved by using only 10,000 labeled training sentences chosen by the proposed active learning strategy.

  
   

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