Topic mining based event cluster acquisition method

Abstract

The present invention discloses a topic mining based event cluster acquisition method. The method comprises the following steps: S1, collecting a text data set C; S2, preprocessing the text data set C, and removing meaningless words from the text data set; S3: setting the number n of topics and a parameter, running CTM to obtain a CTM model; S4, in a covariance matrix sigma indicating an association degree between topics of the CTM model, searching for all maximum clusters by using a backtracking algorithm, wherein the maximum clusters are topic clusters; and S5, for each topic comprised in each topic cluster, selecting a most corresponding article from the text data set C, and clustering events corresponding to the most corresponding article to form an event cluster. The method provided by the present invention has the following advantages: association degree information at the topic level is used during association degree analysis, and compared with the traditional technology in which association degree information is mined and used at the word level, the method provided by the present invention can better improve rationality in calculating an event association degree.

Description

technical field [0001] The invention belongs to the field of computer text processing and mining, and relates to a hierarchical theme model mining technology, in particular to a method for acquiring event clusters based on theme mining. Background technique [0002] With the rapid development of science and technology, the way of dissemination of information has undergone earth-shaking changes. In particular, the popularization of Internet technology and the growing influence of the Internet have made network information the main means for people to obtain information. With more and more text information on the Internet, how to mine effective event information from text information has become a big challenge. Based on this practical requirement, there is a need for a technology that can extract events automatically, accurately and in real time. Existing machine learning techniques can solve the problem of mining events from text, and most of them use topic models to mine i...

AI Technical Summary

Problems solved by technology

Therefore, Blei, DavidM and Lafferty, JohnD pointed out that one of the defects of LDA in Acorrelated topic model of science (CTM) [5] is that it cannot directly measure the degree of correlation between topics

Images