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Semi-supervised Chinese named entity recognition method based on deep learning

A named entity recognition and deep learning technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem of low recognition accuracy, achieve the effect of improving recognition accuracy, ensuring accuracy, and improving performance

Active Publication Date: 2018-12-07
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0007] Aiming at the problem that the existing Chinese named entity recognition method has a low accuracy rate of Chinese text named entity recognition in a specific field, the present invention provides a semi-supervised Chinese named entity recognition method based on deep learning, which improves the accuracy and recall of named entity recognition Rate

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  • Semi-supervised Chinese named entity recognition method based on deep learning
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[0039] In order to better understand the technical solutions in this application, the following will give a clear and detailed description of this application in combination with the drawings and specific implementation methods in the embodiments of this application.

[0040] In the semi-supervised Chinese named entity recognition method based on deep learning, there are two functional components: a learner and a scorer.

[0041] ●The learner is a supervised learning model for named entity recognition; the present invention adopts a neural network model based on deep learning, and can learn a more effective feature representation by constructing a model with a multi-layer neural network.

[0042] The scorer is a machine learning model that performs two classifications (that is, credible labels and noise labels) on the results marked by the learner. The credible label refers to the high-confidence label produced by the learner, assuming that it is the same as the manual labeling...

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Abstract

The invention belongs to the information extraction technology in the artificial intelligence field, and provides a semi-supervised Chinese named entity recognition method based on deep learning for the Chinese text in the specific field with only a small amount of labeled data and a large amount of unlabeled data. Specifically, the method includes the following steps: firstly, constructing a character-based deep learning named entity recognition model; secondly, design a scoring device, train a learning device and a scoring device by using that annotated data; thirdly, designing a semi-supervised learning framework to realize semi-supervised Chinese named entity recognition using unlabeled data. The invention can improve the accuracy and recall rate of the Chinese named entity recognitionin the specific field.

Description

technical field [0001] The invention belongs to the information extraction technology in the field of artificial intelligence, especially for Chinese texts in a specific field with only a small amount of labeled data and a large amount of unlabeled data, and can improve the accuracy and recall rate of automatically extracted named entities and their types. Background technique [0002] The task of named entity recognition (Named Entity Recognition, NER) is mainly to identify and classify proper names such as names of people, places, and institutions that appear in the text. basis of the task. As different fields have customized requirements for named entity recognition, higher requirements are placed on the accuracy and recall of recognition. For example, for general proper nouns, a more detailed division is required, and place names are divided into country names, provinces / states, city names, street names, etc. For named entity recognition in specific fields, such as ext...

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Application Information

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IPC IPC(8): G06F17/27G06F17/30G06N99/00
CPCG06F40/295
Inventor 李东升李真真冯大为
Owner NAT UNIV OF DEFENSE TECH
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