Some Issues in Application of NLP to Intelligent Information Retrieval System and Guidelines for its Solution

Authors

  • Shrey Patel  B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India

Keywords:

Natural Language Processing, Information Retrieval, Document Retrieval, Word Sense Disambiguation, Query, Machine Learning

Abstract

The process of getting the semantic information out of vast text data available is not easy. Many of the recent work on Intelligent Information Retrieval (IIR) dealt with usage of natural language processing (NLP), but the results were not so encouraging. Even currently applicable state-of-the-art NLP gives only moderate results when used in document retrieval systems. Firstly, this research paper addresses the key issues that occur when incorporating NLP techniques in IIR. We look in detail, what are the causes of issues. Then we propose some solutions to tackle with these issues, for getting better IIR system outputs.

References

  1. Brants, T. (2003, September). Natural Language Processing in Information Retrieval. In CLIN.
  2. Voorhees, E. M. (1999). Natural language processing and information retrieval. In Information Extraction (pp. 32-48). Springer, Berlin, Heidelberg.
  3. Vossen, P.(1998) EuroWordNet: a multilingual database with lexical semantic networks, Kluwer Academic Publishers, Norwell, MA, USA.
  4. Shestakov, D. (2014). Intelligent Web Crawling. Intelligent Informatics, 5.
  5. Mohanty, R., Dutta, A., & Bhattacharyya, P. (2005, May). Semantically relatable sets: building blocks for representing semantics. In MT Summit (Vol. 5).
  6. Voorhees, E. M. (1993, July). Using WordNet to disambiguate word senses for text retrieval. In Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 171-180). ACM.
  7. Petasis, G., Karkaletsis, V., Paliouras, G., Krithara, A., & Zavitsanos, E. (2011). Ontology population and enrichment: State of the art. In Knowledge-driven multimedia information extraction and ontology evolution (pp. 134-166). Springer Berlin Heidelberg.
  8. Petasis, G., Vichot, F., Wolinski, F., Paliouras, G., Karkaletsis, V., & Spyropoulos, C. D. (2001, July). Using machine learning to maintain rule-based named-entity recognition and classification systems. In Proceedings of the 39th Annual Meeting on Association for Computational Linguistics (pp. 426-433). Association for Computational Linguistics.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shrey Patel, " Some Issues in Application of NLP to Intelligent Information Retrieval System and Guidelines for its Solution, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.712-716, March-April-2018.