Class center vector text classification method based on dependency, word class and semantic dictionary

Abstract

The invention relates to text classification of natural language processing, and specifically relates to a class center vector text classification method based on dependency, word class and a semanticdictionary. To overcome the semantic defect of a feature selection algorithm based on statistics, the invention introduces the dependency, the semantic dictionary and the word class to optimize and cluster text features, provides an improved weight calculation formula, and further provides an improved class center vector text classification method. The text classification method of the inventionhas advantages of both high classification efficiency of a traditional class center vector method and high classification precision of a K-nearest neighbor algorithm, and can be widely used in variousclassification systems.

Description

technical field

[0001] The invention relates to text classification in natural language processing, in particular to a class center vector text classification method based on dependency relationship, part of speech and semantic dictionary. Background technique

[0002] With the rapid development of computer technology, especially in the context of the "Internet +" era, network information such as documents, pictures, audio and video has exploded exponentially, and a large number of electronic files exist in the form of electronic files in daily life. How to obtain the desired information from massive data is a hot and difficult point in current research, and text classification is one of the important research directions.

[0003] Text classification is an important research direction in text processing technology, which began in the 1950s. It is a comprehensive technology integrating linguistics, mathematics, computer science and cognitive science. In the late 1950s, H.P. ...

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