Chinese tourism field named entity identification method based on graph convolution neural network

Abstract

According to the Chinese tourism field named entity identification method based on the graph convolution neural network, the graph convolution neural network comprises an input layer, an embedded layer, a graph convolution layer and a hierarchical structure, and an input body comprises a named entity and a non-entity; the method comprises the following steps: S1, simultaneously expanding to two sides by taking any non-entity of a tourism domain text as a center until a single character in a complete sentence is traversed; S2, extracting character features; S3, extracting character features; S4, inputting and training; S5, optimizing a graph convolution layer; S6, labeling all named entities in the tourism field text data, introducing a Laplace regularization loss function into the graph convolution layer so as to mine node internal structure information and extract character features; and S7, obtaining a hierarchical relationship between the named entity and the non-entity. According to the method, the character feature extraction method is constructed by using the graph convolution neural network, and semantic modeling is performed on the character features so as to realize correct identification of the named entities in the text.

Description

technical field [0001] The invention relates to the field of named entity recognition methods in the field of cultural tourism, in particular to a named entity recognition method in the field of Chinese tourism based on a graph convolutional neural network. Background technique [0002] Named entity recognition refers to the extraction of entities such as names of people, places, or organizations from a large amount of unstructured or structured text, and their precise classification and identification. However, the traditional named entity recognition method relies heavily on linguistic knowledge and feature engineering, which makes it ignore the potential information hidden in the entities in the text, thus increasing the difficulty of identifying named entities in the text. [0003] In the field of natural language processing morphological analysis, with the improvement of human living standards, travel and tourism are an indispensable part of our lives. People pay more ...

AI Technical Summary

Problems solved by technology

[0008] Although the above researchers use deep learning algorithms to solve the errors caused by manual participation in setting features and improve the accuracy of named entity recognition, most of them are based on a single word embedding technology to extract the word vector features of the text, and this method will not only lose the semantic information of named entities in the text, but also ignore the hierarchical information between named entities in the text

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