融合 ＰＡＭ 和主题偏好 ＴｅｘｔＲａｎｋ 的历史沿革信息抽取.

Aiming al the problem of the information extraction of history evolution, this paper proposed a method combined PAM with topical TextRank . PAM could fetch the distribution of different topics and the distribution of topics and words. Based on the distribution modeled by the PAM. topical TextRank could extract the keywords which were more relevant to historical evolution topic. Because historical evolution topic was complex and highly relevant to other topics. W hether the words were relevant to the historical evolution topic was not clear and hardly confirmed. So. PAM got both the distribution of topics and the distribution of topics and words, which was very useful to solve those problems. Furthermore, the historical evolution topic facilitated topical TextRank to extract the words which were more relevant to historical evolution topic. The result shows that the method combined the PAM and topical TextRank is more effective to extract the topic information of history evolution. [ABSTRACT FROM AUTHOR]