Chinese named entity recognition method, device, computer equipment and storage medium

Abstract

The present application relates to a Chinese named entity recognition method, comprising: obtaining a sentence to be recognized, inputting the sentence to be recognized into an embedding layer of a preset neural network, and outputting an output word vector of the sentence to be recognized; Synchronously input the preset bidirectional long-term short-term memory network and the preset empty convolution network to obtain the output matrix; input the output matrix into the preset segmented long-term short-term memory network to obtain multiple prediction sequences; use conditional random field algorithm An optimal sequence is selected from the predicted sequence, and the identified entity is obtained according to the optimal sequence. In this application, by synchronously adopting bidirectional long-term short-term memory network and preset hollow convolution network for feature transformation, features can be effectively extracted without relying on entity dictionaries, and recognition efficiency is improved.

Description

technical field [0001] The present application relates to the field of information processing, in particular to a Chinese named entity recognition method, device, computer equipment and storage medium. Background technique [0002] Named Entity Recognition (NER for short), also known as "proper name recognition", refers to the recognition of entities with specific meanings in text, mainly including names of people, places, institutions, and proper nouns. Named entity recognition is a basic task in natural language processing tasks, and its effects will directly affect tasks such as entity linking, machine translation, and relationship extraction. [0003] Since Chinese does not have a natural separator to segment each word, character-based Chinese named entity recognition is a better choice than word-based methods. Commonly used named entity recognition methods include BiLSTM (Bi-directional Long Short-Term Memory, two-way long-term short-term memory) combined with CRF (Con...

AI Technical Summary

Problems solved by technology

Commonly used named entity recognition methods include BiLSTM (Bi-directional Long Short-Term Memory, two-way long-term short-term memory) combined with CRF (Conditional randomfields, conditional random field) and other methods. Types of tags are combined to form an entity, this method relies on a larger entity dictionary, however, when a larger entity dictionary is added, the recognition efficiency is low

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