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This study introduces complex networks methods to enhance natural language processing (NLP) tasks. By combining network topology with traditional text analysis, hybrid approaches significantly improve NLP performance.

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Area of Science:

  • Computational Linguistics
  • Network Science
  • Data Science

Background:

  • Statistical methods are common for language analysis.
  • Complex and dynamical systems offer new language modeling approaches.
  • Few studies link physical system properties to improved NLP task performance.

Purpose of the Study:

  • To develop complex networks methods for enhancing NLP.
  • To integrate topological text properties with traditional descriptions.
  • To improve the performance of current statistical NLP methods.

Main Methods:

  • Devised complex networks methods for NLP.
  • Employed a fuzzy classification strategy.
  • Extracted topological properties from texts.

Main Results:

  • Topological text properties complement traditional descriptions.
  • Hybrid approaches (network + traditional) outperformed individual methods.
  • The proposed model demonstrated improved NLP task performance.

Conclusions:

  • Complex networks offer a valuable framework for NLP.
  • Hybrid methods combining topology and text analysis are effective.
  • The generic model can be applied to diverse textual applications.