Multi-subject extracting method based on semantic categories

Abstract

The invention provides a multi-subject extracting method based on semantic categories. The multi-subject extracting method based on the semantic categories comprises the following steps that firstly, a document is preprocessed according to a traditional method and a vector composed of feature words is obtained preliminarily; secondly, synonyms are merged by the utilization of the corresponding relation between word meanings and concepts of 'HowNet', polysemic word disambiguation is carried out according to the correlation between the semantic categories and the context, and a concept vector model is constructed to represent the document; then the concept vector model is converted to be a semantic category model according to the one-to-one corresponding relation between the concepts and the semantic categories; the concept similarity is calculated by the utilization of the related semantic information in the concepts in 'HowNet' and then the semantic similarity is obtained; the semantic categories are clustered by improving the K-means algorithm according to the method of presetting seeds, and a plurality of subject semantic category clusters are formed; finally, a plurality of sub-subject word sets are obtained in a reverse mode according to the corresponding relations between the semantic categories and the concepts and between the concepts and words. The method considers the semantic information, overcomes the defect that the sensibility to the initial center by the K-means algorithm and time-and-space cost are not stable, and improves the quality of extracted subjects.

Description

technical field [0001] The invention relates to the technical field of text information extraction, in particular to a semantic class-based multi-theme extraction method. Background technique [0002] Since human society entered the information age, various electronic texts have emerged in large numbers. Among these massive texts, there are a large number of multi-theme texts, which contain rich and diverse subject information. For example: a report on Premier Li Keqiang’s visit to Europe is a political news, but also economic news. With the development of science and technology, the degree of integration between disciplines is getting higher and higher, most of the research spans multiple disciplines, and many scientific and technological texts contain multiple topics from different aspects, such as a text on biological gene information mining , covering both computer science and biomedical domain topics. Therefore, there are a large number of multi-theme texts in the rea...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is that in order to solve the problem that traditional text processing technology is based on word frequency statistics, a text can only propose a single topic. In order to solve the problems of low algorithm efficiency and low quality of extracted keywords caused by context information, a multi-topic extraction method based on semantic classes is provided. This method utilizes the semantic knowledge base of "HowNet" to perform feature words representing text One-to-one mapping, the text is represented as a concept model, and synonyms are automatically merged into the same concept during the mapping process, realizing vector dimensionality reduction; polysemy words appearing in the text are disambiguated according to the correlation between semantic classes and context

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