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Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures

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Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures


Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia



Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5, August 2021, pp.1429-1442, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf

The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user?s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, “fuzzy membership” between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F- measure then non-fuzzy edge weights.

Information Retrieval; Fuzzy Log; WordNet; Centrality


IJTSRD45074
Volume-5 | Issue-5, August 2021
1429-1442
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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