tailieunhanh - Báo cáo khoa học: "Discriminative Pruning of Language Models for Chinese Word Segmentation"

This paper presents a discriminative pruning method of n-gram language model for Chinese word segmentation. To reduce the size of the language model that is used in a Chinese word segmentation system, importance of each bigram is computed in terms of discriminative pruning criterion that is related to the performance loss caused by pruning the bigram. Then we propose a step-by-step growing algorithm to build the language model of desired size. Experimental results show that the discriminative pruning method leads to a much smaller model compared with the model pruned using the state-of-the-art method. At the same Chinese word. | Discriminative Pruning of Language Models for Chinese Word Segmentation Jianfeng Li Haifeng Wang Dengjun Ren Guohua Li Toshiba China Research and Development Center 5 F. Tower W2 Oriental Plaza East Chang An Ave. Dong Cheng District Beijing 100738 China lijianfeng wanghaifeng rendengjun liguohua @ Abstract This paper presents a discriminative pruning method of n-gram language model for Chinese word segmentation. To reduce the size of the language model that is used in a Chinese word segmentation system importance of each bigram is computed in terms of discriminative pruning criterion that is related to the performance loss caused by pruning the bigram. Then we propose a step-by-step growing algorithm to build the language model of desired size. Experimental results show that the discriminative pruning method leads to a much smaller model compared with the model pruned using the state-of-the-art method. At the same Chinese word segmentation F-measure the number of bigrams in the model can be reduced by up to 90 . Correlation between language model perplexity and word segmentation performance is also discussed. 1 Introduction Chinese word segmentation is the initial stage of many Chinese language processing tasks and has received a lot of attention in the literature Sproat et al. 1996 Sun and Tsou 2001 Zhang et al. 2003 Peng et al. 2004 . In Gao et al. 2003 an approach based on source-channel model for Chinese word segmentation was proposed. Gao et al. 2005 further developed it to a linear mixture model. In these statistical models language models are essential for word segmentation disambiguation. However an uncom pressed language model is usually too large for practical use since all realistic applications have memory constraints. Therefore language model pruning techniques are used to produce smaller models. Pruning a language model is to eliminate a number of parameters explicitly stored in it according to some pruning criteria. The goal of