计算机科学 ›› 2010, Vol. 37 ›› Issue (12): 156-160.

• 人工智能 • 上一篇    下一篇

基于多层最大嫡模型的句子主干分析

葛斌,封孝生,谭文堂,肖卫东   

  1. (国防科技大学C4ISR技术国防科技重点实验室 长沙410073)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(60903225,60772012),湖南省自然科学基金项目(03JJY3110)资助.

Skeleton Parsing Based on Multi-layer Maximum Entropy Model

GE Bin,FENG Xiao-sheng,TAN Wen-tang,XIAO Wei-dong   

  • Online:2018-12-01 Published:2018-12-01

摘要: 句子主干分析的主要任务是自动识别句子的主干成分。鉴于汉语句子之间成分的相关性,提出一种多层最大嫡模型,它的底层最大嫡利用句子的上下文特征识别主千词候选项,高层最大嫡利用底层最大嫡模型的计算结果,结合句子内的远距离特征和句子之间的关系,对底层最大嫡模型识别出的主干词候选集进行分析。实验证明,该模型对于简单的主干成分识别正确率较高,对训练语料有一定的依赖;随着语料规模的增长,模型性能缓慢提升。

关键词: 最大墒,多层最大嫡模型,主干词,主干分析,自然语言理解

Abstract: The main task of Skeleton Parsing is to identify the skeleton of a sentence automatically. Chinese Skeleton Parsing is a key problem in NLP. Because of the interrelation of the skeleton in the same context, a Multi-layer Maximum Entropy Modcl(MMEM) for the skeleton parsing was proposed. The low-layer ME analyzed skeleton by the context features while the high-layer ME analyzed skeleton by both the result of the low-layer ME and the features between sentences. The experiment showed that MMEM was efficient for Chinese skeleton parsing. A high precision was achieved under a small corpus while it was dependable on the scale of corpus. With the increasing of the corpus, the precilion of MMEM improves slowly.

Key words: Maximum entropy, Multi-layer maximum entropy model, Skeleton word, Skeleton parsing, Natural language processorg

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