Word sense disambiguation method and device based on graph expansion structure, equipment and medium

Abstract

The invention relates to the field of artificial intelligence, and provides a word sense disambiguation method and device based on a graph expansion structure, equipment and a medium, which can extract candidate nodes in an initial graph according to a created window, realize the expansion of the graph, and then improve the connectivity. The weight of each edge is calculated according to the text similarity between the two candidate nodes corresponding to each edge and the word meaning similarity between the two candidate nodes corresponding to each edge, and the importance of each candidate node is determined according to the weight of each edge, so that high semantic and emotion information is explored and captured, and the word meaning disambiguation accuracy is improved; and according to the importance degree of each candidate node, interference removal processing is performed on the candidate map to obtain a target map, so that the word sense disambiguation efficiency is improved through interference removal processing, center measurement is carried out on the target map to obtain a target word sense, and the improvement of the word sense disambiguation effect is realized. In addition, the invention also relates to a block chain technology, and the target map can be stored in a block chain node.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a word sense disambiguation method, device, device and medium based on a graph extension structure. Background technique [0002] The main goal of Natural Language Processing (NLP) is to make machines more intelligently aware of how humans use natural language expressions to perform challenging language tasks. In natural language, many terms, concepts and words can have multiple meanings in different contexts. Therefore, it is difficult to identify the actual meaning of a word without a clearly identifiable context (i.e. the sentence describing the scene). Usually, at least one additional word is needed to provide enough information to be able to determine the actual meaning of the target word. This way of identifying the intent of a word is called word sense disambiguation. meaning to achieve word sense disambiguation. In many natural language processi...

AI Technical Summary

Problems solved by technology

[0006] Among them, the similarity-based method is to judge the correct meaning of a polysemous word by calculating the semantic similarity between each available meaning of a polysemous word and other words in a given context, and it is necessary to consider the meaning assigned to adjacent ambiguous words at the same time. has certain limitations

[0007] However, the structure of the graph-based methods mainly adopted at present may not be fully connected, because some relevant meanings of the target words may not be related to the same domain linguistically (i.e., semantically or lexically), this low-connectivity problem, namely knowledge Incomplete, which can lead to disambiguation of ambiguous words without considering adjacent semantically related words, resulting in incoherent meanings that can lead to misunderstandings

That is to say, words represent nodes, and there is a lack of connection (crossover) between semantically related words, so the lexical relationships that map these nodes are independent of each other, making it impossible to identify the meaning of each word in a given context, thus making it impossible to analyze The possible emotional relationship between ambiguous words affects the accuracy of processing results

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