Entity linking method and device, equipment and storage medium

A technology of entity and entity pair, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as difficult training, few training resources, and large number of parameters, so as to improve accuracy and recall rate and reduce training time Effect

Pending Publication Date: 2021-06-25
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0002] In related technologies, the entity linking method based on graph neural network usually has a large amount of parameters and is difficult to train, and there are a

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  • Entity linking method and device, equipment and storage medium
  • Entity linking method and device, equipment and storage medium
  • Entity linking method and device, equipment and storage medium

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Embodiment Construction

[0034] Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0035] Information Extraction technology refers to the automatic extraction of structured information from unstructured data, and its subtasks include named entity recognition (Named Entity Recognition), entity linking (EntityLinking), downstream relationship extraction and event extraction, etc. .

[0036] The purpose of the entity linking task is to link entity menti...

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Abstract

The invention provides an entity linking method and device, equipment and a storage medium, and relates to the field of natural language processing and knowledge maps. The specific implementation scheme comprises the steps: obtaining a plurality of candidate entities related to mention based on a knowledge base, and obtaining a plurality of mention candidate entity pairs; respectively carrying out feature extraction processing on the mention and the candidate entities to obtain mention features and candidate entity features of each mention candidate entity pair; based on the mention feature and the candidate entity feature of each mention candidate entity pair, constructing a dynamic hypergraph, and performing disambiguation processing by using the dynamic hypergraph to obtain a target entity corresponding to the mention. According to the technical scheme provided by the invention, the training time of the neural network is shortened, and the high-level semantic association between the mention and the candidate entity is deeply mined, so that the accuracy and recall rate of the entity linking task are improved.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, in particular to the fields of natural language processing and knowledge graph. Background technique [0002] In related technologies, the entity linking method based on the graph neural network usually has a large amount of parameters and is difficult to train, and there are also problems such as less training resources, more data noise, and entity knowledge base updating over time, which affects the accuracy of the entity linking task. and recall rate. Contents of the invention [0003] The present disclosure provides a method, device, device and storage medium for entity linking. [0004] According to an aspect of the present disclosure, a method for linking entities is provided, including: [0005] Based on the knowledge base, obtain multiple candidate entities related to the mention, and obtain multiple mention candidate entity pairs; [0006] Perform feature ex...

Claims

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Application Information

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IPC IPC(8): G06F40/295G06F40/134G06N3/04G06N3/08
CPCG06F40/295G06F40/134G06N3/08G06N3/045
Inventor 甘婧儒骆金昌王海威陈坤斌和为王树徽
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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