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NLPIR: A theoretical framework for applying natural language processing to information retrieval

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  • Lina Zhou
  • Dongsong Zhang

Abstract

The role of information retrieval (IR) in support of decision making and knowledge management has become increasingly significant. Confronted by various problems in traditional keyword‐based IR, many researchers have been investigating the potential of natural language processing (NLP) technologies. Despite widespread application of NLP in IR and high expectations that NLP can address the problems of traditional IR, research and development of an NLP component for an IR system still lacks support and guidance from a cohesive framework. In this paper, we propose a theoretical framework called NLPIR that aims at integrating NLP into IR and at generalizing broad application of NLP in IR. Some existing NLP techniques are described to validate the framework, which not only can be applied to current research, but is also envisioned to support future research and development in IR that involve NLP.

Suggested Citation

  • Lina Zhou & Dongsong Zhang, 2003. "NLPIR: A theoretical framework for applying natural language processing to information retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(2), pages 115-123, January.
    • Handle: RePEc:bla:jamist:v:54:y:2003:i:2:p:115-123
    DOI: 10.1002/asi.10193
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    Cited by:

    1. Jorge Iván Pérez Rave & Gloria Patricia Jaramillo Álvarez & Juan Carlos Correa Morales, 2023. "Psycho-managerial text mining (PMTM): a framework for developing and validating psychological/managerial constructs from a theory/text-driven approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 777-808, December.
    2. Quan Lin & Yingchang Huang & Ruojin Zhu & Yue Zhang, 2019. "Comparative Analysis of Mission Statements of Chinese and American Fortune 500 Companies: A Study from the Perspective of Linguistics," Sustainability, MDPI, vol. 11(18), pages 1-18, September.

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