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A survey on different dimensions for graphical keyword extraction techniques: Issues and Challenges.

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This study surveys Graphical Keyword Extraction Techniques (GKET) using Graph of Words (GoW) to enhance Automatic Keyword Extraction (AKE) in Natural Language Processing (NLP). It identifies interdisciplinary research directions for network science and NLP.

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Area of Science:

  • Natural Language Processing (NLP)
  • Network Science
  • Computational Linguistics

Background:

  • The COVID-19 pandemic accelerated the shift from offline to online activities, increasing the importance of Automatic Keyword Extraction (AKE) from textual data.
  • Graphical Keyword Extraction Techniques (GKET), utilizing Graph of Words (GoW), are increasingly explored for analyzing textual information.
  • Existing literature shows diverse approaches to GKET, necessitating a comprehensive survey.

Purpose of the Study:

  • To conduct a comprehensive survey of Graphical Keyword Extraction Techniques (GKET) across various domains.
  • To analyze different dimensions of GKET, including GoW representation, statistical properties, structural stability, diversity of approaches, and node ranking.
  • To identify research gaps and propose future interdisciplinary research directions at the intersection of network science and NLP.

Main Methods:

  • A comprehensive literature survey of GKET was performed, focusing on Graph of Words (GoW) representations and analysis.
  • Experimental analysis compared existing GKET methods across 21 datasets.
  • Word Co-occurrence Networks (WCN) were analyzed for 15 languages and across different genres.

Main Results:

  • Identified strong correspondences in disciplinary approaches for GoW representation (e.g., 'Line Graphs', 'Bigram Words Graphs') and feature extraction (e.g., 'Random Walk', 'Spectral Clustering').
  • Analysis of WCN revealed insights into language and genre-specific structures.
  • The study highlighted the need for integrating multiple dimensions for improved GKET.

Conclusions:

  • GKET offers a promising avenue for advancing Automatic Keyword Extraction (AKE) through network science principles.
  • Integrating diverse approaches and dimensions in GKET can lead to more robust and adaptable NLP solutions.
  • Future research should focus on interdisciplinary studies to handle challenges like streaming data and language independence in NLP.