Named entity identification method, device, medium and equipment

Abstract

Embodiments of the present application disclose a named entity recognition method, a device, equipment, and a medium, wherein, the method includes: obtaining a text to be recognized; word segmentationprocessing being carried out on the text to be recognized to obtain a word segmentation sequence; inputting the word segmentation sequence to a named entity recognition model, and obtaining attributeidentifiers of named entities corresponding to each word segmentation output from the named entity recognition model; furthermore, the named entity in the text to be recognized being determined according to the attribute identification of the named entity corresponding to each participle. The named entity recognition model used in this method is based on feedforward neural network with simple network structure and fewer network parameters, which ensures that the model is easy to maintain and update. In addition, based on the multi-dimensional segmentation features that can fully and comprehensively express the semantic information of segmentation, the model determines the attribute identification of named entity corresponding to each segmentation, which ensures the accuracy of named entity recognition. In addition, the present application also provides a method and apparatus for training a named entity recognition model.

Description

technical field [0001] The present application relates to the technical field of natural language processing, and in particular to a named entity recognition method, device, device, and computer-readable storage medium. Background technique [0002] Named Entity Recognition (NER), also known as "proper name recognition", refers to the recognition of entities with specific meanings in texts, mainly including names of people, places, institutions, nouns of works, network words with specific meanings, etc. proper nouns etc. Named entity recognition plays an important role in information extraction, question answering systems, syntax analysis, machine translation and other application fields. [0003] The currently commonly used named entity recognition solution is to use the named entity recognition model to recognize the text to identify the named entities in it, wherein the named entity recognition model includes a bidirectional long-short-term memory network and a condition...

AI Technical Summary

Problems solved by technology

Since each long-short-term memory network LSTM in the named entity recognition model includes three gate structures, the network structure of the gate structure is complex, and there are many network parameters. If you want the model to achieve better recognition results, you need to rely on large-scale training samples. , the training of the model also consumes too much training time, and it is difficult to conduct a quick test for practical application

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