Abstract: Overlapping ambiguity is one of the key problems in Chinese words segmentation.In this paper，a new hybrid strategy which integrates rule-based method and statistical-based method is presented for solving the overlapping ambiguity.Firstly，rule-set is constructed automatically through error-driven learning which will be used for some ambiguities detection and resolution.Secondly，a score function based on N-Gram language model is constructed.Lastly，a rule-based module and a statistical-based module will be combined for solving all ambiguities detected by FMM&BMM and the rule-set.The experiments show that this hybrid method is more suitable for ambiguities detection and possesses the advantages of both rule-based and statistical-based methods for overlapping ambiguities resolution in Chinese words segmentation.

Key words: overlapping ambiguity, disambiguation rules, statistical language model, score function, full segmentation

摘要： 针对交集型歧义这一汉语分词中的难点问题，提出了一种规则和统计相结合的交集型歧义消歧模型。首先，根据标注语料库，通过基于错误驱动的学习思想，获取交集型歧义消歧规则库，同时，利用统计工具，构建N-Gram统计语言模型；然后，采用正向/逆向最大匹配方法和消歧规则库探测发现交集型歧义字段；最后，通过消歧规则库和评分函数进行交集型歧义的消歧处理。这种基于混合模型的方法可以探测到更多的交集型歧义字段，并且结合了规则方法和统计方法在处理交集型歧义上的优势。实验表明，这种方法提高了交集型歧义处理的精度，为解决交集型歧义提供了一种新的思路。

关键词: 交集型歧义, 消歧规则, 统计语言模型, 评分函数, 全切分