关键词: 广义话题结构, 新支话题, 自动识别, 最大熵模型

Abstract:

We focus on the task of the automatic recognition,which identify whether an absent topic of a punctuation clause is the subject or object of its previous sentence. We regard this task as the pointcut of the automatic recognition of absent topics in Chinese punctuation clauses. Several literal features and semantic features are summerized to achieve this task by combining the rules and the maximum entropy model. Experimental results show that Fscore of this recognition approach reaches 82% for the samples of some specific verbs. Experimental results analysis shows that verb features and semantic features play the most important role in the recognition process; neither rules nor statistics can be neglected, and refined knowledge has great influence on the performance of the recognition .

Key words: generalized topic structure;new branch topic;automatic recognition;maximum entropy model