An Unsupervised Method for Entity Mentions Extraction in Chinese Text

Abstract

Entities play an important role in many natural language applications. Based on the Automatic content Extraction (ACE) conference, we study the extraction technologies of entity mentions in Chinese text. Compared to named entities, entity mentions have rich categories and complex structures, which bring great difficulty to the extraction task. To solve the above problems, we propose an unsupervised method to detect entity mentions and identify their categories in Chinese text, namely Un-MenEx. With the abundant data of Baidu Baike and Baidu search, Un-MenEx exploits a similarity calculation method to extract entity mentions in text, which solves the problem of identifying rare entity names difficultly and optimizes the mentions segmented wrongly. Moreover, Un-MenEx can meet the demand of processing massive data by reason of no manual annotation data. We conduct the experiments with the news text, and the experimental results show that this method has practical application value, and ensure the accuracy requirement.