中文分词的混合模型

LDV Forum Pub Date : 2007-07-01 DOI:10.21248/jlcl.22.2007.90
Xiaofei Lu
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引用次数: 2

摘要

本文描述了一种将机器学习与语言学和统计启发式相结合的混合模型,用于将未知词识别与中文分词相结合。该模型由两个主要组件组成:一个标记组件,它用字符位置(POC)标签标注中文句子中的每个字符,表明其在单词中的位置;一个合并组件,将POC标记的字符序列转换为分词句。标注组件使用基于支持向量机(Vapnik, 1995)的标注器对文本进行初始标注,使用基于变换的标注器(Brill, 1995)对初始标注进行改进。除了分配给字符的POC标签外,合并组件还结合了许多语言和统计启发式方法来检测具有规则内部结构的单词、识别长单词和过滤非单词。实验表明,在不使用单独的未知词识别机制的情况下,该模型在分词方面的f值达到95.0%,在未知词识别方面的竞争召回率达到74.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Model for Chinese Word Segmentation
This paper describes a hybrid model that combines machine learning with linguistic and statistical heuristics for integrating unknown word identification with Chinese word segmentation. The model consists of two major components: a tagging component that annotates each character in a Chinese sentence with a position-of-character (POC) tag that indicates its position in a word, and a merging component that transforms a POC-tagged character sequence into a word-segmented sentence. The tagging component uses a support vector machine (Vapnik, 1995) based tagger to produce an initial tagging of the text and a transformation-based tagger (Brill, 1995) to improve the initial tagging. In addition to the POC tags assigned to the characters, the merging component incorporates a number of linguistic and statistical heuristics to detect words with regular internal structures, recognize long words, and filter non-words. Experiments show that, without resorting to a separate unknown word identification mechanism, the model achieves an F-score of 95.0% for word segmentation and a competitive recall of 74.8% for unknown word identification.
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