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Research on the structure function recognition of PLOS.

Jiangfeng Liu1,2, Zhixiao Zhao3,4, Na Wu3,4

  • 1School of Information Management, Nanjing University, Nanjing, China.

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|February 8, 2024
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Summary
This summary is machine-generated.

Deep learning models, particularly SciBERT, excel at identifying discourse structure in scientific texts. Natural language processing (NLP) enhances text mining and scientific communication efficiency.

Keywords:
BERTPLOSdeep learninginformetricsstructure function recognition

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Area of Science:

  • Computational Linguistics
  • Bioinformatics
  • Scientific Communication

Background:

  • Scientific literature contains complex discourse structures crucial for information retrieval.
  • Automated methods are needed to efficiently parse and understand these structures.

Purpose of the Study:

  • To evaluate deep learning models for identifying discourse structure and functional features in scientific text.
  • To explore the application of natural language processing (NLP) in text mining and scientific communication.

Main Methods:

  • Utilized the PLOS literature series for full-text data.
  • Employed four deep learning models: BERT, RoBERTa, SciBERT, and SsciBERT for structure-function recognition.

Main Results:

  • SciBERT demonstrated superior performance, achieving the highest F1 score among the evaluated models.
  • The model showed strong performance in recognizing structures within 'method' and 'result' paragraphs.

Conclusions:

  • Deep learning models, especially SciBERT, effectively recognize discourse-level structure and function in scientific literature.
  • NLP techniques offer significant potential for improving literature management, retrieval, and accelerating scientific progress.