传统的分词器在微博文本上不能达到好的性能，主要归结于: ( 1) 缺少标注语料; ( 2) 存在大量的非规范化词． 针对这两类问题，文中提出一个分词和文本规范化的联合模 型，该模型在迁移分词基础上，通过扩充迁移行为来实现文本规范化，进而对规范的文本 进行分词． 在实验中，采用大量的规范标注文本及少量的微博标注文本进行训练，实验结 果显示，该模型具有较好的域适应性，其分词错误率比传统的方法减少了 10． 35% ． 

Traditional word segmentation methods fail to achieve good performance on microblog texts，which can
be attributed to the lack of annotated corpora and the existence of a large number of informal words． In order to solve the two kinds of problems，a joint model of word segmentation and text normalization is proposed． In this model，on the basis of the transition-based word segmentation，the texts are normalized by extending transition actions and then the words are segmented on the normalized texts． By experiments，the proposed model is trained on both a large number of annotated standard corpora and a small number of microblog corpora． The results show that the proposed model is of better domain adaptability，and it reduces the error rate of word segmentation by 10. 35% in comparison with traditional methods．