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FSN: Joint Entity and Relation Extraction Based on Filter Separator Network.

Qicai Dai1,2, Wenzhong Yang1,2, Fuyuan Wei1,2

  • 1School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.

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|February 23, 2024
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Summary
This summary is machine-generated.

This study introduces a novel joint entity and relation extraction method, FSN, to address feature imbalance between subtasks. FSN improves information filtering and feature merging for more accurate relational triple extraction from complex texts.

Keywords:
BERTdynamic loss functionlocal feature extractionsubtask interaction balance

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Machine Learning

Background:

  • Joint entity and relation extraction methods are crucial for extracting structured information from text.
  • Existing methods often suffer from imbalanced feature interactions between Named Entity Recognition (NER) and Relation Extraction (RE) subtasks.
  • This imbalance hinders optimal performance in extracting relational triples.

Purpose of the Study:

  • To propose a novel joint entity and relation extraction method, FSN, that addresses the feature interaction imbalance between NER and RE subtasks.
  • To enhance the extraction of local feature information for both NER and RE.
  • To improve the overall accuracy of relational triple extraction.

Main Methods:

  • Developed a Filter Separator Network (FSN) module using a two-direction LSTM for information filtering and merging.
  • Introduced Named Entity Recognition Generation (NERG) and Relation Extraction Generation (REG) modules inspired by Transformer decoders and average pooling.
  • Implemented a dynamic loss function to adjust subtask learning weights adaptively.

Main Results:

  • The proposed FSN model demonstrated improved performance in joint entity and relation extraction.
  • The NERG and REG modules effectively captured entity and relation boundary information.
  • Experimental results on SciERC and ACE2005 datasets showed satisfactory performance compared to baseline models.

Conclusions:

  • The FSN method effectively mitigates the feature interaction imbalance in joint entity and relation extraction.
  • The novel NERG and REG modules enhance the capture of crucial local features.
  • The dynamic loss function contributes to narrowing the gap between ideal and realistic extraction results, offering a promising approach for complex text analysis.