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• W2139649463 abstract "This paper details the participation of the Australian e- Health Research Centre (AEHRC) in the ShARe/CLEF 2013 eHealth Evaluation Lab { Task 3. This task aims to evaluate the use of infor- mation retrieval (IR) systems to aid consumers (e.g. patients and their relatives) in seeking health advice on the Web. Our submissions to the ShARe/CLEF challenge are based on language models generated from the web corpus provided by the organisers. Our baseline system is a standard Dirichlet smoothed language model. We enhance the baseline by identifying and correcting spelling mistakes in queries, as well as expanding acronyms using AEHRC's Medtex medical text analysis platform. We then consider the readability and the author- itativeness of web pages to further enhance the quality of the document ranking. Measures of readability are integrated in the language models used for retrieval via prior probabilities. Prior probabilities are also used to encode authoritativeness information derived from a list of top-100 consumer health websites. Empirical results show that correcting spelling mistakes and expanding acronyms found in queries signicantly improves the eectiveness of the language model baseline. Readability priors seem to increase retrieval eectiveness for graded relevance at early ranks (nDCG@5, but not pre- cision), but no improvements are found at later ranks and when consid- ering binary relevance. The authoritativeness prior does not appear to provide retrieval gains over the baseline: this is likely to be because of the small overlap between websites in the corpus and those in the top-100 consumer-health websites we acquired." @default.
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• W2139649463 date "2013-09-01" @default.
• W2139649463 modified "2023-10-06" @default.
• W2139649463 title "Retrieval of health advice on the web: AEHRC at ShARe/CLEF eHealth Evaluation Lab Task 3" @default.
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