基于最大熵模型的评价搭配识别*

在分析酒店评论文本倾向性过程中，针对某些评价词语所产生的歧义性问题，提出一种基于最大熵的评价搭配识别的方法。该方法通过构建极性词表，挖掘出评价词语类别作为语义特征，将其与词、词性、距离、否定词特征结合构成最大熵的复合模板，采用最大熵模型进行评价搭配识别。实验结果证明，采用构建的最大熵复合模板进行评价搭配识别具有较高的准确率和识别性能。

Identification of evaluation collocation based on maximum entropy model

In the process of analyzing the orientation of hotel comment, some opinion-bearing words may cause ambiguity. This paper proposed a method of evaluation collocation identification based on maximum entropy. This method designed a sentiment word table, mined the category of opinion-bearing words as semantic feature, combined this feature with lexical, part-of-speech, position and negative adverbs to construct a compound template, and then employed maximum entropy model to implement evaluation collocation identification. Experimental results show that the accuracy and recognition performance are higher when using the compound template constructed to identify evaluation collocation.