Residual network and attention mechanism-based drug relationship extraction method

A relationship extraction and attention technology, applied in chemical machine learning, special data processing applications, instruments, etc., can solve the problems of poor robustness and the inability of neural network relationship extraction models to highlight the importance, so as to improve robustness and classification. Improve the effect and achieve the effect of classification

Inactive Publication Date: 2018-09-04
ANQING NORMAL UNIV
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Problems solved by technology

However, similar to the previous feature-based relationship extraction system, the neural network relationship extraction model cannot highlight the importance of different drug entities in the relationship description and has poor robustness.

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  • Residual network and attention mechanism-based drug relationship extraction method
  • Residual network and attention mechanism-based drug relationship extraction method
  • Residual network and attention mechanism-based drug relationship extraction method

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[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. The drug relationship extraction method based on the residual network and the attention mechanism proposed by the present invention comprises the following steps:

[0029] S1. Use the word vector trained by word2vec to represent the word vector in the drug entity relationship dataset;

[0030] S2. In order to mine the dependence between long-distance words in the drug relationship description and overcome the gradient dispersion problem, a two-layer bidirectional lon...

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Abstract

The invention discloses a residual network and attention mechanism-based drug relationship extraction method. The method comprises the following steps of: S1, carrying out vector representation on words in a drug entity relationship data set; S2, carrying out time-series modeling on a drug relationship statement by utilizing a two-layer bidirectional long-short term memory model neural network; S3, importing residual connection into the constructed two-layer bidirectional long-short term memory model neural network; S4, decomposing a deep semantic meaning automatically obtained by the two-layer bidirectional long-short term memory model neural network into a memory space and an attention space, fusing memory information and attention information, and inputting the fused information into aSoftmax classifier to extract a drug relationship. According to the drug relationship extraction method disclosed by the invention, dependency relationships between long-distance words are effectivelysolved, gradient diffusion is overcome, model overfitting is prevented, the model robustness is good and the classification effect is good.

Description

technical field [0001] The invention relates to the technical field of drug relationship extraction, in particular to a drug relationship extraction method based on a residual network and an attention mechanism. Background technique [0002] Drug relationship extraction means that when two drugs are taken at the same time, one drug will inhibit or stimulate the performance of the other drug. According to the research and investigation of Sahu et al. [1], more and more people choose to take multiple drugs at the same time, and the interaction between multiple drugs will cause harm to human health (arXiv preprintarXiv: 1701.08303, 2017). Therefore, in order to avoid drug abuse accidents, it is necessary to construct a data model reflecting drug interactions and design and develop a decision system for drug relationship extraction. At the same time, with the explosive growth of information in the biological field, there is an urgent need for an automatic way to collect and min...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16C20/70
Inventor 刘娟黄忠汪祝芬唐飞
Owner ANQING NORMAL UNIV
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