Co-occurrence latent semantic vector space model semantic core method based on literature resource topic clustering

Abstract

The invention belongs to the technical field of a semantic vector space model semantic core method, and particularly relates to a co-occurrence latent semantic vector space model semantic core method based on literature resource topic clustering. The method mainly solves the problems that an existing semantic vector space model semantic core method is high in semantic information extraction complexity, is insufficient in semantic information extraction, is high in model dimension, is high in complexity on the aspects of time and space when the existing semantic vector space model semantic core method is applied to a clustering algorithm and the like. The co-occurrence latent semantic vector space model semantic core method based on the literature resource topic clustering comprises the following steps that: 1) preprocessing literature data; 2) carrying out word frequency statistics on an extracted keyword for subsequently establishing a co-occurrence matrix to be used; 3) taking whether the keyword is in the presence in the literature or not as a weight to construct a vector space model shown by the literature; 4) constructing a co-occurrence latent semantic vector space model; 5) constructing a semantic core function; and 6) carrying out literature clustering.

Description

technical field [0001] The invention belongs to the technical field of a semantic kernel method of a semantic vector space model, and in particular relates to a semantic kernel method of a document resource subject clustering co-occurrence latent semantic vector space model. Background technique [0002] The era of big data has brought us a large number of unstructured text resources. As an unsupervised machine learning method, clustering is one of the main means to realize text resource mining. Text clustering is different from general data clustering. It first needs to represent the text information in a data structure. The basic model of text representation is the vector space model (VSM), which maps each document into a high-dimensional sparse vector in the text space, so the semantic similarity calculation problem between texts can be transformed into It is the calculation of vectors in the vector space, that is, by calculating the similarity between vectors to measure...

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Problems solved by technology

[0004] The present invention mainly aims at the semantic kernel method of the current semantic vector space model, which has relatively large semantic information extraction complexity, insufficient semantic information extraction, high model dimension, and high time and space complexity when applied to clustering algorithms, etc. problem, providing a semantic kernel method for text resource topic clustering co-occurrence latent semantic vector space model

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