一种基于字词联合解码的中文分词方法

近年来基于字的方法极大地提高了中文分词的性能,借助于优秀的学习算法,由字构词逐渐成为中文分词的主要技术路线.然而,基于字的方法虽然在发现未登录词方面有其优势,却往往在针对表内词的切分效果方面不及基于词的方法,而且还损失了一些词与词之间的信息以及词本身的信息.在此基础上,提出了一种结合基于字的条件随机场模型与基于词的Bi-gram语言模型的切分策略,实现了字词联合解码的中文分词方法,较好地发挥了两个模型的长处,能够有效地改善单一模型的性能,并在SIGHAN Bakeoff3的评测集上得到了验证,充分说明了合理的字词结合方法将有效地提高分词系统的性能,可以更好地应用于中文信息处理的各个方面.

Approach to Chinese Word Segmentation Based on Character-Word Joint Decoding

The performance of Chinese word segmentation has been greatly improved by character-based approaches in recent years. With the help of powerful machine learning strategies, the words extraction via combination of characters becomes the focus in Chinese word segmentation researches. In spite of the outstanding capability of discovering out-of-vocabulary words, the character-based approaches are not as good as word-based approaches in in-vocabulary words segmentation with some internal and external information of the words lost. In this paper we propose a joint decoding strategy that combines the character-based conditional random field model and word-based Bi-gram language model, for segmenting Chinese character sequences. The experimental results demonstrate the good performance of our approach, and prove that two sub models are well integrated as the joint model of character and word could more effectively enhance the performance of Chinese word segmentation systems than any of the single model, thus is fit for many applications in Chinese information processing.