Word vector generation method and device, computer storage medium and electronic equipment

Abstract

The invention provides a word vector generation method and device, a computer storage medium and electronic equipment. The method comprises the steps of: obtaining an associated text composed of associated text units of a context window of a target text unit in each statement, mining a text set composed of all the associated texts by utilizing a document topic generation model to obtain topic distribution vectors corresponding to the target text units of all the statements; and configuring the same semantic tags for the target text units with relatively high topic distribution vector similarity, configuring different semantic tags for the target text units with relatively low topic distribution vector similarity, and training a word vector model by using the training corpus set added withthe semantic tags to obtain a word vector of each semantic tag, wherein the topic distribution vector corresponding to the target text unit can reflect the context of the target text unit, the semantics of the target text unit in different statements can be distinguished according to the similarity of the topic distribution vector, and a word vector model is trained based on the distinguishing result to obtain word vectors of different semantics.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, and in particular, to a method, apparatus, computer storage medium and electronic device for generating word vectors. Background technique [0002] Natural language processing is an important direction in the field of artificial intelligence, which usually involves tasks such as sentiment analysis, intelligent question answering systems, and machine translation. However, computer programs cannot directly process words or words in natural language. In order for computers to understand natural language, a necessary link is to use the corresponding word vector to represent each word or word contained in natural language, so that the above can be achieved by processing the word vector subsequently. Task. [0003] The prior art generally directly uses a corpus set (including multiple sentences) to train an existing word vector (Word2Vec) model, so as to obtain word vectors...

AI Technical Summary

Problems solved by technology

However, there are polysemous words in natural language, that is, a word can represent different semantics in different contexts, and the existing methods can only generate a unique word vector for a word, which cannot reflect the different semantics of polysemous words, resulting in the subsequent implementation based on word vectors. have poor accuracy on natural language processing tasks

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