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Joint extraction model of entity relations based on decomposition strategy.

Ran Li1, Kaijun La2, Jingsheng Lei2

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This study introduces a novel joint extraction model for natural language processing, improving accuracy in identifying entities and relationships by decomposing the task and using a pointer mechanism.

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Machine Learning

Background:

  • Joint entity-relationship extraction models aim to improve performance by reducing cascading errors through joint learning.
  • Existing parameter-sharing models still face challenges with pipeline influences, entity information redundancy, and overlapping entity recognition.

Purpose of the Study:

  • To propose a novel joint extraction model that overcomes limitations of existing methods.
  • To enhance the accuracy of entity and relationship identification in natural language processing tasks.

Main Methods:

  • A decomposition strategy is employed, dividing the joint extraction task into two stages: head entity identification and tail entity/relationship identification.
  • A hierarchical model is utilized for improved accuracy in the second stage.
  • A pointer mechanism is introduced to capture joint features of entity boundaries and relationship types for boundary-aware classification.

Main Results:

  • The proposed model demonstrates superior performance on both the NYT and WebNLG datasets.
  • The decomposition strategy and pointer mechanism effectively address entity information redundancy and overlapping entity recognition.

Conclusions:

  • The novel joint extraction model significantly improves entity and relationship extraction accuracy.
  • The decomposition strategy and pointer mechanism offer a promising direction for advancing joint extraction in natural language processing.