四种基本统计句法分析模型在汉语句法分析中的性能比较

A Comparative Study of Four Primary Statistical Models in Chinese Parsing

统计模型的选择是统计句法分析的关键。目前句法分析常用的有四种经典统计模型—PCFG模型 ,基于历史模型、分层渐近式模型和头驱动模型。本文通过实验 ,在已有的 10 0 0 0句汉语树库基础上 ,测试了这四种经典模型在现有数据规模下各自的性能 ,并论述了这四种经典模型的各自特点。本文旨在通过对四种基本模型的比较研究 ,为具体应用中句法分析模型的选择提供参考和依据。

Choosing the statistical model is the key problem in statistical parsing. Statistical model lies in the core of NLP parsing. This paper investigates 4 primary statistical parsing models, namely PCFG, history-based model, cascading parsing model and head-driven parsing model, and compares their performances in a 10000 Chinese treebank. The analysis based on the experiment were shown in the paper. The comparative study of these models can be exploited to build the practical and effective Chinese parser.