Abstract: The performance of statistical methods for Chinese word segmentation is limited owing to lack of the specific training corpus, and the dictionary-based methods are affected by unknown words and segmentation ambiguities. To realize domain adaptation, an approach combined statistical methods and a domain dictionary is developed. The approach firstly builds a high quality domain dictionary, and uses a statistical method to obtain preliminary results. Then, an algorithm for eliminating ambiguity is designed based on rules and Chinese character subsets with defined properties. Experimental results on a construction law domain corpus show that the precision, the recall and F-measure achieve 92.08%, 94.26% and 93.16%. The approach combined with new word detection can improve the performance of unknown words processing.

Key words: Chinese word segmentation, domain specific, ambiguity resolution, domain dictionary, construction law

摘要： 在专业领域分词任务中，基于统计的分词方法的性能受限于缺少专业领域的标注语料，而基于词典的分词方法在处理新词和歧义词方面还有待提高。针对专业领域分词的特殊性，提出统计与词典相结合的分词方法，完善领域词典构建流程，设计基于规则和字表的二次分词歧义消解方法。在工程法领域语料上进行分词实验。实验结果表明，在工程法领域的分词结果准确率为92.08%，召回率为94.26%，F值为93.16%。该方法还可与新词发现等方法结合，改善未登录词的处理效果。

关键词: 中文分词, 专业领域, 歧义消解, 领域词典, 工程法