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Medical named entity recognition method and system

A named entity recognition and entity recognition technology, applied in the field of medical text processing, can solve the problems of inability to guarantee the correct prediction of word labels, inability to obtain more accurate word meanings, and less frequency of medical professional vocabulary, so as to improve the efficiency of feature extraction and improve Vocabulary, the effect of improving training performance

Pending Publication Date: 2021-10-08
济南超级计算技术研究院 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although BiLSTM explores a large amount of contextual information, in the existing training word embeddings, medical professional words appear less frequently, cannot obtain more accurate word meanings, and cannot guarantee that the word labels obtained every time are correctly predicted

Method used

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  • Medical named entity recognition method and system
  • Medical named entity recognition method and system
  • Medical named entity recognition method and system

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0038] This embodiment discloses a medical named entity recognition method, through the neural network model of multi-level Transformer and BERT, deep-level word meaning information is mined, thereby improving the accuracy of named entity recognition, such as figure 1 As shown, the method includes the following steps:

[0039] Step 1: Obtain the text data to be recognized and perform preprocessing;

[0040] The preprocessing specifically includes preprocessing such as word segmentation.

[0041] Step 2: Based on the medical named entity recognition model, perform named entity recognition on the text data to be recognized.

[0042] The construction method of the medical named entity recognition model comprises:

[0043] Step A: Obtain a training data set;

[0044] The training data set is text data through word segmentation and pre-marking, and the present embodiment adopts a professional medical corpus as the training data set;

[0045] Step B: Train the named entity recog...

Embodiment 2

[0062] The purpose of this embodiment is to provide a medical named entity recognition system. The system includes:

[0063] A data acquisition module configured to acquire text data to be recognized;

[0064] The named entity recognition module is configured to perform named entity recognition on the text data to be recognized based on the medical named entity recognition model, wherein the medical named entity recognition model includes an input layer, a feature extraction layer and a labeling layer connected in sequence, and the features The extraction layer includes a character embedding module and a word embedding module.

Embodiment 3

[0066] The purpose of this embodiment is to provide an electronic device.

[0067] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the following steps are implemented, including:

[0068] Obtain the text data to be recognized;

[0069] Based on the medical named entity recognition model, the named entity recognition is performed on the text data to be recognized,

[0070] Wherein, the medical named entity recognition model includes an input layer, a feature extraction layer and a labeling layer connected in sequence, and the feature extraction layer includes a character embedding module and a word embedding module.

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Abstract

The invention discloses a medical named entity recognition method and system. The method comprises the steps of obtaining to-be-recognized text data; on the basis of a medical named entity recognition model, carrying out named entity recognition on text data to be recognized, wherein the medical named entity recognition model comprises an input layer, a feature extraction layer and a labeling layer which are connected in sequence, and the feature extraction layer comprises a character embedding module and a word embedding module. According to the method, the sentence in the text is considered from the character level and the word level, the information amount and the meaning of the embedded word are fully obtained, and the recognition precision of the named entity can be improved.

Description

technical field [0001] The invention belongs to the technical field of medical text processing, and in particular relates to a medical named entity recognition method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Named Entity Recognition (NER) is a basic task in the NLP field and an important basic tool for most NLP tasks such as question answering systems, machine translation, and syntax analysis. Previous methods are mainly dictionary-based and rule-based. The dictionary-based method is a method of fuzzy search or complete matching through strings, but as new entity names continue to emerge, the quality and size of the dictionary are limited; the rule-based method is to use the entity name to form its own characteristics and phrases Common collocations, artificially specify some rules and expand the rule set, but it take...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/211G06N3/04G06N3/08
CPCG06F40/295G06F40/211G06N3/08G06N3/047G06N3/044
Inventor 潘景山徐卫志范胜玉涂阳
Owner 济南超级计算技术研究院
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