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A traditional Chinese medicine health consultation text named entity recognition method based on transfer learning

A named entity recognition and transfer learning technology, applied in the application field of natural language sequence labeling, can solve the problems of low recall rate, low accuracy rate, small data volume, etc., to improve the accuracy rate and recall rate, improve the accuracy rate, reduce the Effect of loss value

Active Publication Date: 2019-06-21
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies in the prior art, providing a method for named entity recognition of TCM health consultation text based on transfer learning, which can make full use of Annotated corpus in other fields and unannotated corpus information in this field solve the problem of low accuracy and low recall rate of named entity recognition due to the small amount of tagged corpus data in TCM online health consultation texts

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  • A traditional Chinese medicine health consultation text named entity recognition method based on transfer learning
  • A traditional Chinese medicine health consultation text named entity recognition method based on transfer learning
  • A traditional Chinese medicine health consultation text named entity recognition method based on transfer learning

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Embodiment

[0032] This embodiment provides a method for named entity recognition of TCM health consultation text based on transfer learning, the flow chart of the method is as follows figure 1 shown, including the following steps:

[0033] S1. Constructor, select textual named entity recognition and labeling data sets in other fields that are highly relevant to TCM health consultation named entity recognition tasks, construct a neural network, and pre-train the neural network;

[0034] S2. Construct forward and reverse cyclic neural networks respectively, and use the unlabeled data set of TCM health consultation texts to pre-train the forward and reverse cyclic neural networks respectively to obtain a forward language model and a reverse language model;

[0035] S3. On the basis of the neural network pre-trained in S1, integrate the features of the cyclic neural network layer of the forward language model and the reverse language model in S2, combine the fully connected network layer and...

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Abstract

The invention discloses a traditional Chinese medicine health consultation text named entity recognition method based on transfer learning, which comprises the following steps: S1, selecting a text named entity recognition annotation data set in other fields, constructing a neural network, and performing pre-training; S2, respectively constructing a one-way recurrent neural network, and performinglanguage model training by utilizing the traditional Chinese medicine health consultation text unlabeled data set to obtain a forward language model and a reverse language model. And S3, fusing the loop network layer output characteristics of the unidirectional language model, and combining the full connection network layer and the conditional random field layer to obtain a final named entity recognition model. And S4, naming the entity by using the traditional Chinese medicine health consultation text to identify the labeled data set, and carrying out fine tuning training. According to the method, named entities in other fields can be migrated to identify labeled text knowledge and unlabeled text knowledge in the field, so that the identification accuracy and recall rate of the named entities of the traditional Chinese medicine health consultation text are effectively improved, and the convergence speed of the model is increased.

Description

technical field [0001] The invention relates to the technical field of application of natural language sequence labeling, in particular to a named entity recognition method for TCM health consultation texts based on transfer learning. Background technique [0002] With the rapid development and popularization of the Internet, more and more people choose to conduct online health consultation to doctors in the form of online question and answer on health and medical websites. This method is more convenient and efficient to promote the relationship between doctors and patients. However, in many cases, due to the relative shortage of doctor resources in our country, many patients often cannot get timely professional answers to their online health consultation questions. At the same time, with the application of artificial intelligence technology in text processing, more and more institutions have constructed medical-related knowledge bases. How to automatically obtain the user'...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06N3/04G06N3/08
Inventor 文贵华陈河宏
Owner SOUTH CHINA UNIV OF TECH
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