Improving Search Effectiveness with Field-based Relevance Modeling

Liu, B, Lu, X, Kurland, O and Culpepper, J 2018, 'Improving Search Effectiveness with Field-based Relevance Modeling', in Proceedings of the 23rd Annual Australasian Document Computing Symposium, Dunedin, New Zealand, 11-12 December 2018, pp. 1-4.


Document type: Conference Paper
Collection: Conference Papers

Title Improving Search Effectiveness with Field-based Relevance Modeling
Author(s) Liu, B
Lu, X
Kurland, O
Culpepper, J
Year 2018
Conference name ADCS 2018
Conference location Dunedin, New Zealand
Conference dates 11-12 December 2018
Proceedings title Proceedings of the 23rd Annual Australasian Document Computing Symposium
Publisher ACM
Place of publication New York
Start page 1
End page 4
Total pages 4
Abstract Fields are a valuable auxiliary source of information in semi-structured HTML web documents. So, it is no surprise that ranking models have been designed to leverage this information to improve search effectiveness. We present the first (initial) study of utilizing field-based information in the relevance modeling framework. Fields play two different, and integrated, roles in our models: sources of information for inducing relevance models and units on which relevance models are applied for ranking. Our preliminary results suggest that field-based relevance modeling can improve precision at top ranks; specifically, to a greater extent than the commonly used BM25F and SDM-Fields field-based models. Further analysis shows that using field-based relevance models mainly improves the effectiveness of tail queries. Our findings suggest that using field-based information together with relevance modeling is a promising area of future exploration.
Subjects Data Structures
Information Retrieval and Web Search
Keyword(s) relevance modeling
web search
field-based retrieval models
DOI - identifier 10.1145/3291992.3292005
Copyright notice © 2018 Copyright held by the owner/author(s).
ISBN 9781450365499
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