Method for extracting multiple subject terms from single Chinese text

Abstract

The invention provides a method for extracting multiple subject terms from a single Chinese text. The method comprises the steps that firstly, a traditional method is used for preprocessing the text, and vectors composed of feature words are primarily obtained; secondly, merger is performed on synonyms by means of the corresponding relation between the meaning of the words and concepts in the 'HowNet', disambiguation is performed on polysemous words according to the dependency of semantic types and the context, and a concept vector model is built to represent the text; thirdly, the concept similarity of related semantic information is calculated by means of the concepts in the 'HowNet', the K-means algorithm is improved through a 'seed presetting' method to perform clustering on the concepts, and a plurality of subject concept clusters are formed; fourthly, according to the corresponding relation of the concepts and the words, a plurality of sub-subject terms are obtained. According to the method, semantic information is considered, the defect that the K-means algorithm is not stable in sensibility and space overhead of a primary center, and quality of extracted subjects is improved.

Description

technical field [0001] The invention relates to the technical field of text information extraction, in particular to a method for extracting multiple subject words from a single Chinese text. 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-t...

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 subject words caused by context information, a method for extracting multiple subject words from a single Chinese text is provided. The feature words are mapped one by one, and 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 analyzed according to the correlation between semantic classes and context Disambiguation

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