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This paper examines the need for efficient keyword extraction methods to improve search accuracy and indexing due to the increasing volume of digital information. Traditional approaches, often based on word occurrence counts, tend to lose the contextual meaning of the text. The current work proposes a four-step NLP-based method for keyword extraction, including tokenization, POS tagging, noun phrase extraction, and chunking. The novel contribution of this research is the design of a grammatical pattern for extracting keywords (in the form of noun phrases) without losing the actual meaning of the text. This approach aims to retain the contextual meaning of the text. When compared to the most popular NLTK tool RAKE, the proposed method delivers better results while preserving the contextual meaning of the input text.

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NLP-Based Approach for Efficient Keyword Identification in Unstructured Text

  • 会议论文

  • 发表日期：

    2026年

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  • Minu Choudhary,
  • Sourabh Rungta,
  • Shikha Pandey

摘要

This paper examines the need for efficient keyword extraction methods to improve search accuracy and indexing due to the increasing volume of digital information. Traditional approaches, often based on word occurrence counts, tend to lose the contextual meaning of the text. The current work proposes a four-step NLP-based method for keyword extraction, including tokenization, POS tagging, noun phrase extraction, and chunking. The novel contribution of this research is the design of a grammatical pattern for extracting keywords (in the form of noun phrases) without losing the actual meaning of the text. This approach aims to retain the contextual meaning of the text. When compared to the most popular NLTK tool RAKE, the proposed method delivers better results while preserving the contextual meaning of the input text.