基于句子对齐的汉语句法结构推导的计算模型

A Computational Model for Chinese Syntactic Structure Induction Based on Sentence Alignment

基于句子的相似性,提出了无指导的汉语句法结构推导方法.基本思想是:首先,在汉语句子库的基础上,通过句对之间的对齐,得到交替的相同片断和相异片断.然后,根据相同片断优先或相异片断优先策略,选取相应的对齐片断作为句子成分候选,并对可能因片断交叉而导致边界摩擦的候选进行歧义消解.最后,通过逐步归约句子成分,推导出汉语句法结构树.为了避免对齐过程中词的稀疏问题,还对部分具有明显规律的词事先作了归类处理.分别以词、词性以及词联合词性作为句子基本构成单元,评测了推导的句法结果.测试结果表明:对于3种构成单元,相异片断优先归约得到的结果的F值都超过了46%,均优于相同片断优先归约所得到的结果,最好的达到了49.52%,好于已报道的结果.

This paper introduces an unsupervised learning framework of Chinese syntactic structure based sentences similarity. First, all sentence pairs in the Chinese sentence corpus are aligned, and each pair is partitioned into similarity segmentations and different ones which alternately occur, Then, aligned similarity segmentations or different ones are selected as potential constituent candidates based on the strategy of similarity priority or of difference priority respectively. As the boundary friction may be introduced in the later step, its disambiguation is further carried out. Finally, by inducing sentence constituents, the syntactic structures are learned. In order to reduce word sparseness in the process, some words are replaced by classes in advance. Three forms of the sentence units, such as the sequence of words, the sequence of POS （part of speech）-tags and the sequence of words with POS-tag, are examined and the learned syntactic structures are evaluated respectively. The results show that different priority strategy achieves a better performance than the similarity one, and the Fs are above 46% for all three forms, with the best one being 49.52%, which is better than those having been reported.