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Relation extraction method and system based on ensemble learning

A technology of relation extraction and integrated learning, applied in the fields of natural language processing and deep learning, can solve problems such as attention bias, and achieve the effect of accurate location features and good sentence features

Active Publication Date: 2020-05-08
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the attention bias problem caused by repeated entities in the remote supervised relation extraction in the prior art

Method used

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  • Relation extraction method and system based on ensemble learning
  • Relation extraction method and system based on ensemble learning
  • Relation extraction method and system based on ensemble learning

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Embodiment Construction

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0061] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0062] In order to make the technical solutions and advantages in the examples of the present application clearer, the exemplary embodiments of the present application will be further described in detail below...

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Abstract

The invention relates to the technical field of natural language processing and deep learning, in particular to a method and system for processing repeated entities in remote supervision relation extraction. According to the main technical scheme, the method comprises the steps that corpora form a sentence packet according to entity pairs, and the position of a target entity in a statement containing repeated entities is determined; a word vector containing semantic and position information is construncted; sentence vectors are constructed through a multi-angle convolutional neural network; and through a dynamic routing mechanism, a sentence packet level vector is constructed and sentence packets are classified. According to the relation extraction method and system, the problem of attention deviation caused by repeated entities which are not mentioned in an existing remote supervision relation extraction method and system can be positioned and effectively solved.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and deep learning, in particular to a method and system for relation extraction. Background technique [0002] In a general sense, information extraction is defined as extracting specific real-time information from natural language texts. Its three important subtasks are entity extraction, relation extraction, and event extraction, which are widely used in knowledge graph construction, question answering systems, and other fields. Relation extraction is a key link in information extraction, and its main task is to determine the semantic relationship between entities. In the existing technology, the supervised learning algorithm or the remote supervised learning algorithm in the neural network is mainly used, and the relation extraction task is regarded as a classification task. [0003] Almost all current mainstream relation extraction models introduce multi-instance learning...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/75G06F40/289G06F40/30G06K9/62G06N3/04G06N20/20
CPCG06F16/75G06N20/20G06N3/045G06F18/214
Inventor 孙新姜景虎蔡琪侯超旭盖晨尚煜茗
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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