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Keyphrase Extraction from Scientific Articles

Author

Listed:
  • Navitha Abhinaya S
  • Neha H
  • Papireddigari Renusree
  • Sowmya Lakshmi B. S

Abstract

Keyphrase extraction is a crucial task in natural language processing (NLP) that involves identifying important terms and phrases in a text. This paper presents a methodology for extracting keyphrases from scientific articles using a combination of preprocessing techniques and the term frequency-inverse document frequency (TF-IDF) algorithm. The approach includes tokenization, stopword removal, and punctuation elimination, followed by the application of the TF-IDF vectorizer to identify and score keyphrases. The results demonstrate the effectiveness of the method in highlighting significant terms in scientific texts.

Suggested Citation

  • Navitha Abhinaya S & Neha H & Papireddigari Renusree & Sowmya Lakshmi B. S, 2024. "Keyphrase Extraction from Scientific Articles," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 601-611, June.
  • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:239
    DOI: 10.32628/CSEIT24103210
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24103210
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