Keyphrase Extraction from Scientific Articles
DOI:
https://doi.org/10.32628/CSEIT24103210Keywords:
Keyphrase Extraction, Natural Language Processing, TF-IDF, Scientific Articles, Text PreprocessingAbstract
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.
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