Method for calculating entity relevance based on word net

Abstract

The invention relates to the field of word net processing, and particularly provides a method for calculating entity relevance based on a word net. The method comprises the following steps: S1, a keyword acquisition component; S2, an entity space positioning assembly; and S3, an entity relationship calculation component. Compared with the prior art, the method has the advantages that the scatteredunits are marked in a unified mode on the basis, an intelligent discrete unit analysis system is constructed, the mining analysis and processing functions of discrete units are effectively achieved,correlation analysis of low-correlation data is achieved, and the method has good popularization values.

Description

technical field [0001] The invention relates to the field of word net processing, and specifically provides a method for calculating entity relevance based on word net. Background technique [0002] With the continuous expansion of computer application fields, people attach great importance to natural language processing. Application requirements such as machine translation, language recognition, and information retrieval have put forward higher and higher requirements for the natural language processing capabilities of computers. In order for a computer to process natural language, it first needs to be modeled. Natural language modeling methods have undergone a transformation from basic rule-based methods to basic statistical methods. In the context of research on statistical language models, Google released Word2vec, a software tool for training word vectors, in 2013. [0003] Word2vec can quickly and efficiently express a word into a vector form through an optimized tr...

AI Technical Summary

Problems solved by technology

Traditional analysis cannot be analyzed due to the large number of data sources, uneven data quality, and lack of field and research direction data

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