Automatic Query Expansion for Term Selection with BERT Score and WordNet Semantic Filtering

Authors

  • Rishabh Sachan  M.Tech. Scholar, Department of Computer Science and Engineering, Naraina College of Eng. and Tech., Kanpur, Uttar Pradesh, India
  • Mrs Sony Tripathi  Assistant Professor, Department of Computer Science and Engineering, Naraina College of Eng. and Tech., Kanpur, Uttar Pradesh, India

Keywords:

Information Retrieval, Query Extension, Pseudo Relevance Feedback, WordNet, Word2vec, Query Expansion, Theme Semantic Network

Abstract

In this modern era, with loads and tons of data, extracting a much relevant data has always been the user’s desire. This whole process of retrieving the relevant document is known as information retrieval. Information retrieval system uses query expansion techniques for retrieving the most relevant document for satisfying the user’s requirements. There have been several query expansions introduced few of which are knowledge based, corpus based, and pseudo relevance feedback. They make use of synonyms accordingly user’s query with a purpose of extracting the relevancy and it also screens the documents that contains the search term but has a completely different meaning. A combination of genetic algorithms and fuzzy logic blocks approach is proposed in this work. We also presented a new method that attenuates the power of knowledge based or corpus based techniques.

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

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Published

2021-06-30

Issue

Volume 8, Issue 4, May-June-2021

Section

Research Articles

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

How to Cite

[1]
Rishabh Sachan, Mrs Sony Tripathi, " Automatic Query Expansion for Term Selection with BERT Score and WordNet Semantic Filtering" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.132-142, May-June-2021.