Word vector learning model training method and server

Abstract

Embodiments of the invention provide a word vector learning model training method and a server. The method comprises the following steps of: obtaining a word meaning vector and a primitive vector corresponding to a word in a training sample text; expressing a word vector in the training sample text according to the word meaning vector or the primitive vector; and substituting the word vector expressed by the word meaning vector or the primitive vector into an existing word vector learning model and carrying out model training. The server is used for executing above method. According to the word vector learning model training method and the server provided by the invention, the word vector is expressed through the primitive vector or the word meaning vector, and the word vector learning model corresponding to the word vector expressed by the word meaning vector or the primitive vector is trained, so that the semantic information of the word can be sufficiently expressed.

Description

technical field [0001] The embodiment of the present invention relates to the technical field of machine learning, and in particular to a training method and server for a word vector learning model. Background technique [0002] A sememe is the smallest semantic unit of a word, and a limited number of sememes can be used to describe the semantics of a large number of Chinese words. For each word, the sememe possessed by the word is not intuitively displayed. In order to conduct corresponding research, some researchers have manually marked each word, and assigned a word to each word through linguistic knowledge. or multiple sememes, and thus form a knowledge base HowNet (HowNet). [0003] Word vector refers to the vectorization of words. Word vectors include different dimensions. Each dimension describes a feature of a word. Converting words into word vectors can help computers understand natural language. The existing technology uses word The vector learning model is train...

AI Technical Summary

Problems solved by technology

However, the word vector in the above word vector learning model is not related to the smallest semantic unit of the word - sememe, therefore, it cannot fully express the semantic information of the word

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