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Entity recognition model training method and device, equipment and storage medium

An entity recognition and training method technology, applied in the field of artificial intelligence, can solve the problem of not being able to recognize and evaluate online speech in time, and achieve the effect of reducing computational complexity, easy identification, and reducing the number of

Active Publication Date: 2021-04-30
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The main purpose of this application is to provide a training method for the entity recognition model, aiming to solve the technical problem of not being able to recognize and evaluate the pronunciation of online speech in time

Method used

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  • Entity recognition model training method and device, equipment and storage medium
  • Entity recognition model training method and device, equipment and storage medium
  • Entity recognition model training method and device, equipment and storage medium

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Embodiment Construction

[0053] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0054] refer to figure 1 , the training method of the entity recognition model of an embodiment of the present application, comprising:

[0055]S1: Obtain an incompletely labeled specified training sample, wherein the specified training sample is any sample in the incompletely labeled dataset;

[0056] S2: Input the specified training samples into the probability prediction model to obtain the label probabilities respectively corresponding to all the unlabeled texts in the specified training samples;

[0057] S3: According to the label probabilities corresponding to all ...

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Abstract

The invention relates to the field of artificial intelligence, and discloses an entity recognition model training method, which comprises the steps of obtaining an incompletely labeled specified training sample; inputting the specified training sample into a probability prediction model to obtain label probabilities corresponding to all unlabeled characters in the specified training sample; according to the label probabilities corresponding to all the unlabeled characters in the specified training sample, obtaining a label sequence with the highest probability through calculation by means of a Viterbi algorithm; according to the label sequence with the highest probability, determining covering labels respectively corresponding to all unlabeled characters in the specified training sample; obtaining a label sequence set corresponding to the specified training sample according to the covering label; obtaining label sequence sets corresponding to all the training samples in the incomplete annotation data set according to the obtaining mode of the label sequence sets corresponding to the specified training samples; and under the constraint of a preset loss function, training an entity recognition model through the label sequence sets corresponding to all the training samples. And a real tag sequence can be identified more easily.

Description

technical field [0001] The present application relates to the field of artificial intelligence, in particular to a training method, device, device and storage medium for an entity recognition model. Background technique [0002] The training of entity recognition models relies on a large amount of fully labeled data, but high-quality labeled data usually requires very professional labelers, making it difficult and expensive to obtain training data. In order to save costs, we can use incompletely labeled data to train entity recognition models. Incompletely labeled data means that only some entities in the text are labeled, while other unlabeled content may be non-entities or entities. In order to improve the effect of using incompletely labeled data to train entity recognition models, all label sequences that meet the text labeling conditions are usually taken into account in model training, and the probability distribution information of all possible label sequences is esti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06F40/295
CPCG06N3/08G06F40/295G06F18/2415G06F18/214
Inventor 阮鸿涛郑立颖胡沛弦徐亮
Owner PING AN TECH (SHENZHEN) CO LTD
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