Word definition generation method based on recurrent neural network and latent variable structure

Abstract

The invention relates to a word definition generation method based on a recurrent neural network and a latent variable structure in the field of natural language processing. On the basis of a recurrent neural network, a variational auto-encoder (VAE) is used for modeling paraphrases, latent variable characteristics are combined. The paraphrases are extracted according to context information of defined words to generate paraphrases of the words, and the method specifically comprises the steps of establishing and arranging a basic corpus; selecting a synonym set of the defined words, and expanding the basic corpus to form a final corpus; carrying out expansion reconstruction on the word vectors of the defined words; constructing a structure model based on the recurrent neural network and thelatent variable; training a latent variable structure model based on a recurrent neural network; and inputting the to-be-paraphrased words and the context information thereof into the trained model to realize semantic paraphrasing of the to-be-paraphrased words in a specific context, thereby achieving polysemy.

Description

technical field [0001] The invention relates to a word definition generation method based on a cyclic neural network and a latent variable structure, belonging to the field of natural language processing. Background technique [0002] English learning dictionary refers to a reference book specially designed for learners whose mother tongue is not English, trying to help learners understand and use English correctly. At present, the definitions of words in most English learning dictionaries have problems such as circular interpretation and too difficult words, which are not conducive to users' understanding. [0003] The main task of Definition Generation is to automatically generate natural language interpretations of words, thereby reducing the time and cost of manual compilation of dictionaries, involving Linguistics, Natural Language Processing, Artificial Intelligence ) and many other fields. [0004] Word vectors, that is, distributed word representations, use low-dim...

AI Technical Summary

Problems solved by technology

The present invention utilizes variational autoencoder (VAE) to model the interpretation on the basis of the cyclic neural network, combines latent variable features, extracts the meaning of the word according to the context information of the defined word to generate the interpretation of the word, and makes up for the existing There is a method that cannot combine the shortcomings of the context, thus solving the problem of polysemy

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