基于条件随机场的汉语动宾搭配自动识别

CRFs Based Recognition of Chinese Verb-Object Collocation

该文提出一种基于机器自动学习的统计模型条件随机场的方法用于汉语动宾搭配的自动识别。实验比较了两种分词与词性标记集下的识别效果,并增加了词性筛选准则作为优化处理。在特征选择上,考察了动词次范畴特征、上下文特征以及它们之间的组合特征的不同实验结果。综合实验结果,基于树库分词和词性标记的最好结果F值是87.40%,基于北京大学标准的分词和词性标记的最好结果F值是74.70%。实验表明,条件随机场模型在词语搭配实例自动识别方面有效可行。

A new method to recognize the Chinese verb-object collocation is proposed on the basis of the conditional random fields （CRFs） model. The CRFs based model is examined with verb subcategorization features, context features, and features of their combination. The experiments are carried on two different Chinese word segmentation and part-of-speech tagging settings, with part-of-speech filtering rules to optimize the experiment. The results show that the best performance is 87.40% in F-score over Tsinghua Chinese Treebank, and 74.70% in F-score over the segmentation and part-of-speech tagging scheme of Peking University. Experimental results show that CRF model is effective in recognizing Chinese verb-object collocation automatically.