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A multilingual entity relationship extraction method and system based on an adversarial training mechanism

An entity-relationship and multi-language technology, applied in the field of information processing, can solve problems such as difficult performance, achieve the effect of improving quality, broad application prospects, and improving the performance of relationship extraction tasks

Active Publication Date: 2021-06-01
TSINGHUA UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0007] In order to overcome the problem that the existing relationship extraction models for single language scenarios are often difficult to achieve better performance in multilingual practical application scenarios, the present invention provides a multilingual entity relationship extraction method and system based on an adversarial training mechanism

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  • A multilingual entity relationship extraction method and system based on an adversarial training mechanism
  • A multilingual entity relationship extraction method and system based on an adversarial training mechanism
  • A multilingual entity relationship extraction method and system based on an adversarial training mechanism

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Embodiment Construction

[0051] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0052] figure 1 It is a schematic diagram of the overall flow of a multilingual entity relationship extraction method based on an adversarial training mechanism in an embodiment of the present invention, as shown in figure 1 As shown, the present invention provides a multilingual entity relationship extraction method based on an adversarial training mechanism, including:

[0053] S1. For any language in multiple languages, obtain a preset number of sentences related to the target entity pair in the language as the target sentence, and construct each target sentence in the independent semantic space corresponding to the language a first sentence vector representation and ...

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Abstract

The present invention provides a multilingual entity relationship extraction method and system based on an adversarial training mechanism, which encodes the target entity pair related target sentences in each language into the independent semantic space corresponding to each language and the consistent semantics corresponding to all languages space, to obtain the independent information of each language and the consistent information across languages ​​contained in the target sentence; and then use the independent attention mechanism of each language and the consistent attention mechanism of each language to measure the relative relationship type of each target sentence. Attention weight, finally combine the attention weights of all target sentences relative to each relationship type to obtain the global probability corresponding to each relationship type, select the maximum probability from the global probability corresponding to each relationship type, and finally get the relationship type corresponding to the maximum probability Predict relationships between target entity pairs. The method and system can deeply utilize the complementarity among multiple languages, and effectively improve the accuracy of the relationship extraction results in a multilingual scene.

Description

technical field [0001] The present invention relates to the technical field of information processing, and more specifically, to a multilingual entity relationship extraction method and system based on an adversarial training mechanism. Background technique [0002] Knowledge graph, also known as knowledge base in some scenarios, is a knowledge system formed after structuring human knowledge in the real world. In a knowledge graph, a large amount of knowledge, such as information in open databases and encyclopedias, is usually expressed in the form of relational data sets. In a relational data set, basic facts are abstracted as entities, and related information such as rules, logic, and reasoning are abstracted as relationships between entities. If entities correspond to points and relationships correspond to edges, then these knowledge can be further presented in the form of graphs, so that they can be efficiently used by computers, and this is the significance of studying...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/211G06N5/02G06N3/04
CPCG06N5/022G06N3/044
Inventor 刘知远王晓智韩旭林衍凯孙茂松
Owner TSINGHUA UNIV
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