基于归约的汉语最长名词短语识别方法

Chinese Maximal Noun Phrase Recognition Based on Reduction

该文提出了最长名词短语(MNP)的操作性定义,分析了其构造和分布特征,并设计了一种基于baseNP归约的识别方法,利用MNP结构特性及起始有定成分、语义核心等语言学特征,缓解了最长名词短语长距离依赖与模型观察窗口受限的矛盾。开放测试取得了88.68%的正确率和89.21%的召回率;归约方法全面提升了识别性能,特别是将多词结构的调和平均值提高1%,优化幅度达6%以上,并且对长距离复杂结构有着更好的识别效果。

This paper proposes an operational definition of Maximal Noun Phrase（MNP）, and then analyzes its structure and distribution features. A MNP recognition based on baseNP reduction is also designed, which exploits the structural characteristics of MNP as well as the linguistic features such as initial definite references and semantic heads. This method eases the conflict between the long distance dependency of MNP and the limits of observation windows in classical models. The experiment indicates a good precision of 88.68% and a recall of 89.21%. The reduction method comprehensively improves system performance, especially it improves Fl-score by 1% and optimal margin by 6 % on multiword MNP, showing its efficiency in complex MNP recognition.