Information extraction method and system based on joint training model

A technology for information extraction and training models, applied in the fields of natural language processing and deep learning, can solve problems such as error transmission, large manpower and time, and low model flexibility, and achieve the effect of improving accuracy
CN110968660AActive Publication Date: 2020-04-07SICHUAN CHANGHONG ELECTRIC CO LTD
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SICHUAN CHANGHONG ELECTRIC CO LTD
Publication Date
2020-04-07

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Abstract

The invention provides an information extraction method and system based on a joint training model, and belongs to the technical field of natural language processing and deep learning, in order to solve the problems that in an existing information extraction technology, a large amount of manpower and time are consumed, and the flexibility of a model is not high, and error transmission is caused, and information extraction is incomplete. The information extraction method comprises the steps: labeling corpora, and obtaining training corpora containing labeling information; sampling the trainingcorpus; converting each character in the sampled corpus into a word vector; inputting the word vector into two deep learning models based on different neural networks for joint training, and iteratively updating neural network parameters of a joint model to obtain a trained information extraction joint model; and inputting a to-be-extracted text into the information extraction joint model, and extracting triple information containing a head entity, a tail entity and an entity relationship.
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Description

technical field

[0001] The invention relates to the technical fields of natural language processing and deep learning, in particular to an information extraction method and system based on a joint training model. Background technique

[0002] With the rapid development of information technology and the continuous upgrading of hardware equipment, there is an increasing demand for using massive data to extract corresponding information from text through deep learning models, and it is applied in various scenarios. Information extraction is to extract structured information from unstructured text. Usually, information extraction tasks are mainly divided into two sub-tasks: entity extraction and relationship extraction. Commonly used methods include rule-based methods and machine learning-based methods. and deep learning-based methods.

[0003] The early information extraction tasks were mainly based on rules and statistics. This method can be divided into two stages: one is to...

Claims

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