Document-level remote supervision relationship extraction method and system

A technology of remote supervision and relation extraction, applied in the field of machine learning, can solve the problem of not directly adapting to document-level relation extraction, etc., and achieve the effect of improving the effect.

Active Publication Date: 2021-02-02
TSINGHUA UNIV +1
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Problems solved by technology

Although early in the sentence-level relation extraction, there have been some works dedicated to denoising distantly supervised corpora by

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  • Document-level remote supervision relationship extraction method and system
  • Document-level remote supervision relationship extraction method and system
  • Document-level remote supervision relationship extraction method and system

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[0043]In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0044]figure 1 This is a schematic flow chart of a document-level remote supervision relationship extraction method provided by an embodiment of the present invention, such asfigure 1 As shown, the embodiment of the present invention provides a document-level remote supervision relationship extraction method, including:

[0045]Step 101: Obtain...

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Abstract

The embodiment of the invention provides a document-level remote supervision relationship extraction method and system. The method comprises the following steps: acquiring remote supervision data, based on a trained pre-noise-reduction model, carrying out noise reduction processing on the remote supervision data to obtain target remote supervision data, acquiring the trained pre-noise-reduction model by training sample remote supervision data marked as a positive sample and sample remote supervision data marked as a negative sample, and inputting the target remote supervision data into a trained text encoder model to obtain a document level relationship extraction result, wherein the trained text encoder model is obtained by training noise-reduced sample document level remote supervision data. According to the embodiment of the invention, noise reduction is carried out on the remote supervision data in a pre-training mode, noise in the remote supervision data can be effectively filtered out, and the model is pre-trained by utilizing large-scale noise-reduced data, so that document-level remote supervision relationship extraction is realized, and the document-level relationship extraction effect is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a document-level remote supervision relationship extraction method and system. Background technique [0002] The task of relation extraction aims to identify the relational facts between entities from text, which is the key to realize the automatic construction of knowledge graph. With the development of deep learning technology, the neural relationship extraction model has been verified in the sentence-level relationship extraction task. However, training a high-quality relationship extraction model requires a large number of manually labeled data sets, and the construction of the data set is also difficult. It takes a lot of time and effort. In order to solve this problem, a remote supervision mechanism is proposed, which realizes automatic labeling of data by aligning knowledge graphs and entities in text, thus providing very large-scale data for relation extraction ...

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Application Information

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IPC IPC(8): G06F16/28G06F16/215G06F40/284G06N3/08
CPCG06F16/288G06F16/215G06F40/284G06N3/08
Inventor 刘知远孙茂松肖朝军姚远谢若冰韩旭林芬林乐宇
Owner TSINGHUA UNIV
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