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Remote supervision relation extraction method based on PGAT and FTATT

A technology of relational extraction and remote supervision, applied in relational databases, medical data mining, computer-aided medical procedures, etc., can solve the problems of noise data relational extraction performance impact, to achieve accurate mining and diagnosis of patient diseases, rich semantics and Grammatical information, closely related effects

Active Publication Date: 2021-03-30
NORTHEASTERN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Nevertheless, noisy data still has a great impact on relation extraction performance

Method used

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  • Remote supervision relation extraction method based on PGAT and FTATT
  • Remote supervision relation extraction method based on PGAT and FTATT
  • Remote supervision relation extraction method based on PGAT and FTATT

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Embodiment Construction

[0054] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Preferred embodiments of the application are shown in the accompanying drawings. However, the present application can be embodied in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the application more thorough and comprehensive.

[0055] The remote supervision relation extraction method based on PGAT and FTATT of this embodiment, such as figure 1 shown, including the following steps:

[0056] Step 1: Get the NYT dataset and preprocess the NYT dataset.

[0057] Sentences containing the same entity pair in the NYT dataset are divided into one package, and the relationship of the entity pair is obtained from the NYT dataset and used as a package label to label the package. According to t...

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Abstract

The invention discloses a remote supervision relation extraction method based on PGAT and FTATT, and relates to the technical field of remote supervision relation extraction. The method comprises thesteps of obtaining an NYT data set, and dividing sentences containing the same entity pair in the data set into a packet; acquiring word vector representation of sentences in each package; extractingsequence features of sentences based on Bi-LSTM; extracting syntactic structure features of sentences based on PGAT; allocating weights to different sentences in the packet by using FTATT; performingweighted summation on the feature vector of each sentence in the packet and the weight coefficient of the sentence to obtain a feature vector of the packet; and according to the feature vector of thepacket, performing relationship classification on the entity pairs in the packet. According to the method, syntactic structure information of sentences can be captured by using PGAT, so that extractedsentence features contain rich information in the aspects of semantics and grammar, an attention mechanism is finely adjusted by adopting FTATT, noise data is dynamically discarded as much as possible, and the accuracy of relationship extraction is improved.

Description

technical field [0001] The present invention relates to the technical field of remote supervision relationship extraction, in particular to a remote supervision relationship extraction method based on PGAT (PiecewiseGraph Attention Network) and FTATT (Fine-tuning Attention Mechanism). Background technique [0002] Knowledge graphs have been widely used in the medical field in recent years. According to patient symptoms, entities of symptoms are matched from medical knowledge graphs. Entities are connected by relationships as edges, such as drug treatment, precautions, and related symptoms. Excavate the disease corresponding to the patient's symptoms and the corresponding treatment measures. For example, a patient has symptoms and signs such as bradykinesia, slow movement, convulsions, fatigue, dementia, depression, etc. In the medical knowledge map, the disease entity corresponding to these symptom entities is Parkinson's disease. The graph consists of (entity, relationship...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/35G06F16/28G06F40/211G06F40/253G06F40/284G06F40/30G16H50/70G06N3/04
CPCG06F16/367G06F16/288G06F16/355G06F40/211G06F40/284G06F40/253G06F40/30G16H50/70G06N3/049Y02D10/00
Inventor 于亚新包健王亚龙吴晓露乔勇鹏刘树越
Owner NORTHEASTERN UNIV
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