Named entity identification method based on neural network and computer storage medium

Abstract

The invention provides a named entity recognition method based on a neural network and a computer storage medium, and the method comprises the steps: inputting a to-be-recognized character string intoa classification model, recognizing the language intention category of the character string through the classification model, and searching an entity label set corresponding to the recognized language intention category from a preset mapping table; inputting the character string into a named entity model to sequentially identify each character in the character string to obtain probability valuesof a plurality of entity tags to which the words belong in the character string; searching an entity label matched with the entity label of the word contained in the character string from an entity label set corresponding to the language intention category, and aiming at the matched entity label, selecting the entity label of which the probability value ranks top N in the matched entity label setas the entity label of the corresponding character. The incorrect entity tags in the named entity model recognition result are filtered through the language intention category recognition result of the classification model, so that the error recognition rate of the named entity model is reduced.

Description

technical field [0001] The invention relates to the technical field of text recognition, in particular to a neural network-based named entity recognition method and a computer storage medium. Background technique [0002] In vehicle-machine NLP (Natural Language Processing, natural language processing), neural network classification models and NER (Named Entity Recognition, named entity recognition models) are usually used to identify text and extract word slots. Generally, the classification model based on CNN (Convolutional Neural Networks, convolutional neural network) can obtain relatively high accuracy and recall rate, and the accuracy can exceed 99.5% in Chinese car machine NLP, so that the text can be more accurately processed Intent recognition, but NER is slightly less accurate in entity recognition, usually only 90% accurate for a piece of text. Contents of the invention [0003] In view of the above problems, the present invention is proposed to provide a named...

AI Technical Summary

Problems solved by technology

Generally, the classification model based on CNN (Convolutional Neural Networks, convolutional neural network) can obtain relatively high accuracy and recall rate, and the accuracy can exceed 99.5% in Chinese car machine NLP, so that the text can be more accurately processed Intent recognition, but NER is slightly less accurate in entity recognition, usually only 90% accurate for a piece of text

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