Chinese pronomial coreference resolution using decision tree plus filter rules

With the advancement of natural language understanding (NLIJ), coreference resolution, as a main part of information extraction (IE), has received much more attention. For this task, either rule-based or statistics-based methods cannot meet the needs of large-scale text processing. An integrated method based on decision tree for Chinese pronominal coreference is proposed. The basic idea is filter rules could, to some degree compensate the drawback of decision tree that ignoring the relationship between attributes. The performance of the proposed method is evaluated on Chinese Treebank. In our experiments, the attributes and coreferences are manually labeled, and then the filter rules are utilized to feature vectors following the decision tree of C4.5 algorithm. The success rate is 82.59%, in which the rate of personal pronouns and demonstrative pronouns are 87.60% and 75.21% respectively.