CORRECTING WORD SEGMENTATION AND PART-OF-SPEECH TAGGING ERRORS FOR CHINESE NAMED ENTITY RECOGNITION

摘要

In the exploration of Chinese named entity recognition for a specific domain, the authors found that the errors caused during word segmentation and part-of-speech (POS) tagging have obstructed the improvement of the recognition performance. In order to further enhance recognition recall and precision, the authors propose an error correction approach for Chinese named entity recognition. In the error correction component, transformation-based machine learning is adopted because it is suitable to fix Chinese word segmentation and POS tagging errors and produce effective correcting rules automatically. The Chinese named entity recognition component utilizes Finite-State Cascades which are automatically constructed by POS rules with semantic constraints. A prototype system, CNERS (Chinese Named Entity Recognition System), has been implemented. The experimental result shows that the recognition performance of most named entities have significantly been improved. On the other hand, the system is also fast and reliable.