The application of NLP in information retrieval

Research Article
Open access

The application of NLP in information retrieval

Xurui Wang 1*
  • 1 Dalian University of Technology    
  • *corresponding author wxr20030331@email.dlut.edu.cn
Published on 23 February 2024 | https://doi.org/10.54254/2755-2721/42/20230795
ACE Vol.42
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-309-8
ISBN (Online): 978-1-83558-310-4

Abstract

The field of Natural Language Processing (NLP) has experienced impressive advancements and has found diverse applications. This paper presents a comprehensive review of the development of NLP in the field of information retrieval. It explores different stages of NLP techniques and methods, including keyword matching, rule-based approaches, statistical methods, and the utilization of machine learning and deep learning technologies. Furthermore, the paper provides detailed insights into the specific applications of NLP in domains such as academic information retrieval, medical information retrieval, travel information retrieval, and e-commerce information retrieval. It analyzes the current state of NLP applications in these domains, highlights their advantages, and discusses their associated limitations. Finally, the paper emphasizes the continuous advancement of the NLP field, with a particular focus on semantic understanding, personalized retrieval, and multimodal information retrieval, to better adapt to diverse data types and user requirements. The paper concludes by summarizing the main points discussed and providing future directions.

Keywords:

NLP, Information Retrieval, Academic Information, Medical Information, Travel Information, E-commerce Information

Wang,X. (2024). The application of NLP in information retrieval. Applied and Computational Engineering,42,290-297.
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References

[1]. Ramos J. Using tf-idf to determine word relevance in document queries, Proceedings of the first instructional conference on machine learning. 2003, 242(1): 29-48.

[2]. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in neural information processing systems, 2017, 30.

[3]. Hakala K, Pyysalo S. Biomedical named entity recognition with multilingual BERT,oceedings of the 5th workshop on BioNLP open shared tasks. 2019: 56-61.

[4]. Ben Abdessalem Karaa W, Alkhammash E H, Bchir A. Drug disease relation extraction from biomedical literature using NLP and machine learning, Mobile Information Systems, 2021: 1-10.

[5]. Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021.

[6]. Abacha A B, Zweigenbaum P. MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies. Information processing & management, 2015, 51(5): 570-594.

[7]. Zhou X, Wang M, Li D. From stay to play–A travel planning tool based on crowdsourcing user-generated contents. Applied geography, 2017, 78: 1-11.

[8]. Orife I, Kreutzer J, Sibanda B, et al. Masakhane--Machine Translation For Africa. arXiv preprint arXiv:2003.11529, 2020.

[9]. Cuizon J C, Agravante C G. Sentiment analysis for review rating prediction in a travel journal, Proceedings of the 4th International Conference on NLP and Information Retrieval. 2020: 70-74.

[10]. Shah S A A, Masood M A, Yasin A. Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM. IEEE Access, 2022, 10: 99633-99645.

Cite this article

Wang,X. (2024). The application of NLP in information retrieval. Applied and Computational Engineering,42,290-297.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-309-8(Print) / 978-1-83558-310-4(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.42
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. Ramos J. Using tf-idf to determine word relevance in document queries, Proceedings of the first instructional conference on machine learning. 2003, 242(1): 29-48.

[2]. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in neural information processing systems, 2017, 30.

[3]. Hakala K, Pyysalo S. Biomedical named entity recognition with multilingual BERT,oceedings of the 5th workshop on BioNLP open shared tasks. 2019: 56-61.

[4]. Ben Abdessalem Karaa W, Alkhammash E H, Bchir A. Drug disease relation extraction from biomedical literature using NLP and machine learning, Mobile Information Systems, 2021: 1-10.

[5]. Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021.

[6]. Abacha A B, Zweigenbaum P. MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies. Information processing & management, 2015, 51(5): 570-594.

[7]. Zhou X, Wang M, Li D. From stay to play–A travel planning tool based on crowdsourcing user-generated contents. Applied geography, 2017, 78: 1-11.

[8]. Orife I, Kreutzer J, Sibanda B, et al. Masakhane--Machine Translation For Africa. arXiv preprint arXiv:2003.11529, 2020.

[9]. Cuizon J C, Agravante C G. Sentiment analysis for review rating prediction in a travel journal, Proceedings of the 4th International Conference on NLP and Information Retrieval. 2020: 70-74.

[10]. Shah S A A, Masood M A, Yasin A. Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM. IEEE Access, 2022, 10: 99633-99645.

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