Named entity recognition model training method, named entity recognition model application method and named entity recognition model training system

A named entity recognition and training method technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of low recognition accuracy and poor recognition effect

Pending Publication Date: 2022-02-08
BEIJING JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a named entity recognition model training method, application method and system thereof, to improve the recognition accuracy and recognition effect of Chinese named entity categories, and to solve the existing named entity recognition methods in the railway field. The problem of low accuracy and poor recognition effect

Method used

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  • Named entity recognition model training method, named entity recognition model application method and named entity recognition model training system
  • Named entity recognition model training method, named entity recognition model application method and named entity recognition model training system
  • Named entity recognition model training method, named entity recognition model application method and named entity recognition model training system

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Embodiment 1

[0072] Such as figure 1 As shown, the present embodiment provides a training method for a named entity recognition model, which specifically includes the following steps:

[0073] Step S1, preprocessing the fault text to obtain word vectors and word vectors; the word vectors include the word vectors of the named entity recognition task and the word vectors of the word segmentation task; the word vectors are used to judge two consecutive words in a sentence Whether the word vectors are related to the same word. Specifically include:

[0074] Step S1.1. Perform word segmentation processing on the fault text to obtain a word segmentation result. Specifically include:

[0075] Step S1.1.1. Segment the fault text data in units of sentences to obtain sentence segmentation results;

[0076] Step S1.1.2, segmenting the sentence segmentation result in units of words to obtain the word segmentation result;

[0077] Step S1.1.3, delete the stop words irrelevant to the named entity r...

Embodiment 2

[0178] Such as Figure 5 As shown, Embodiment 2 provides an application method of a named entity recognition model, which uses the trained named entity recognition model in Embodiment 1. It should be noted that the construction process and training process of the named entity recognition model will not be repeated in this embodiment. The specific process and calculation formula of each step are the same as those in Embodiment 1. Please refer to Embodiment 1, and neither repeat.

[0179] In this embodiment, the specific steps of the application method of the named entity recognition model are as follows:

[0180] Step T1, preprocessing the fault text to obtain word vectors; the word vectors include word vectors for named entity recognition tasks and word vectors for word segmentation tasks;

[0181] Step T2: Input the word vector of the named entity recognition task and the word vector of the word segmentation task into the trained named entity recognition model to obtain the...

Embodiment 3

[0187] This embodiment provides an application system for a named entity recognition model. When the application system is run by a processor, one or more steps in the training method for a named entity recognition model as described in Embodiment 1 are implemented, or, One or more steps in the application method of the named entity recognition model described in Embodiment 2.

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Abstract

The invention relates to a named entity recognition model training method, a named entity recognition model application method and a named entity recognition model training system, and belongs to the field of rail transit natural language processing, and the model training method comprises the following steps: preprocessing a fault text to obtain a word vector and a word vector; wherein the character vectors comprise the character vector of the named entity recognition task and the character vector of the word segmentation task; wherein the word vectors are used for judging whether two continuous word vectors in one sentence are associated into the same word or not; establishing a named entity recognition model; wherein the named entity recognition model comprises a named entity recognition task sub-model, a word segmentation task sub-model and an adversarial training structure; and alternately inputting the character vector of the named entity recognition task and the character vector of the word segmentation task into the adversarial training structure of the named entity recognition model for training to obtain a trained named entity recognition model. When the named entity recognition model is used for recognizing the category of the named entity, the recognition precision and the recognition effect are very high.

Description

technical field [0001] The invention relates to the application of a natural language processing method in the rail transit field, in particular to a training method, an application method and a system for a fault text-oriented named entity recognition model. Background technique [0002] Natural Language Processing (NLP) is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between humans and computers using natural language. Natural language processing is mainly used in machine translation, public opinion monitoring, automatic summarization, opinion extraction, knowledge extraction, text classification, speech recognition, Chinese OCR, etc. Among them, knowledge extraction is the process of extracting implicit and valuable knowledge from textual knowledge sources. In order to effectively mine text information, structured data can be obtained from unstructure...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F40/295G06F40/289G06N3/04G06N3/08
CPCG06F40/295G06F40/289G06N3/08G06N3/047G06N3/044G06F18/214
Inventor 宿帅李若青曹源曲佳谢正光徐会杰楚柏青陈文魏运吕楠豆飞禹丹丹
Owner BEIJING JIAOTONG UNIV
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