基于HMM的汉语介词短语自动识别研究

Research on Automatic Identification for Chinese Prepositional Phrase Based on HMM

提出了一种基于隐马尔可夫模型(HMM)的介词短语界定模型,通过HMM的介词短语边界自动识别和依存语法错误校正2个处理阶段,较好地完成了对一个经过分词和词性标注的句子进行介词短语界定任务,为更进一步的句法分析工作打下良好的基础。试验结果显示:该模型的识别正确率达到了86.5%(封闭测试)和77.7%(开放测试),取得了令人满意的结果。

This paper describes an automatic prediction model of Chinese prepositional phrase boundary location based on HMM. It consists of two stages： automatically identify the phrase boundary using statistics from treebank, then, post-tune the results with dependency grammar knowledge generated by dependency treebank. Experimental results demonstrate a high rate of success for predicting boundary location （86.5% correct rate for close testing and 77.7% for open testing）.