Ranking Health Web Pages with Relevance and Understandability
Author(s) -
João Palotti,
Lorraine Goeuriot,
Guido Zuccon,
Allan Hanbury
Publication year - 2016
Publication title -
proceedings of the 45th international acm sigir conference on research and development in information retrieval
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2911451.2914741
Subject(s) - relevance (law) , readability , computer science , ranking (information retrieval) , rank (graph theory) , benchmark (surveying) , information retrieval , learning to rank , world wide web , mathematics , geodesy , combinatorics , political science , law , programming language , geography
We propose a method that integrates relevance and understandability to rank health web documents. We use a learning to rank approach with standard retrieval features to determine topical relevance and additional features based on readability measures and medical lexical aspects to determine understandability. Our experiments measured the effectiveness of the learning to rank approach integrating understandability on a consumer health benchmark. The findings suggest that this approach promotes documents that are at the same time topically relevant and understandable
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