Named entity identification method, named entity recognition device, storage medium and electronic equipment

Abstract

The invention provides a named entity recognition method, a named entity recognition device, a storage medium and electronic equipment. The method comprises the steps of obtaining a to-be-recognized target text, and determining a target segmented word of the target text; determining a character image of a target character contained in the target segmented word, and determining a glyph vector of the character image; generating a character vector according to the glyph vector and the semantic vector of the target character, and generating a word vector according to all character vectors in the target segmented word; and performing named entity identification processing on the target text based on the word vector of the target segmented word, and determining a named entity in the target text.According to the named entity recognition method, the named entity recognition device, the storage medium and the electronic equipment, glyph features are introduced in the word vector generation process and named entity recognition can be performed more accurately in combination with the glyph features and semantic features. The recognition accuracy can be improved, and the defect of inaccuraterecognition caused by insufficient modeling information of rare words can be overcome.

Description

technical field [0001] The present invention relates to the technical field of named entity recognition, in particular to a method, device, storage medium and electronic equipment for named entity recognition. Background technique [0002] Named entity recognition refers to the identification of entities with specific meaning in text, mainly including names of people, places, institutions, proper nouns, etc. [0003] In order to obtain more accurate results on the task of named body recognition, that is, to identify possible named entities (such as person names, place names, organization names, etc.) in a sentence, the traditional method maps each word in the vocabulary to a Word vectors, and encode the word vectors in the entire sentence through some encoders, and finally predict the label to which each word belongs. [0004] Since many named entities are not commonly used phrases (rare words), it is easy to cause typos when manually inputting named entities. Both represe...

AI Technical Summary

Problems solved by technology

[0004] Since many named entities are not commonly used phrases (rare words), it is easy to cause typos when manually inputting named entities. Both represent personal names; word vectors in traditional methods can only reflect semantic information, and cannot well identify the above-mentioned phrases with similar shapes

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