CLEF (Working Notes) · 2013 · 10 citations · 4 references
ComputingBiomedical InformaticsComputer ScienceBiomedical Text MiningInformation RetrievalNatural Language ProcessingBiomedical Document RetrievalDocument RepresentationHealth Information RetrievalRelevance FeedbackRelevance RankingRetrieval ModelsConcept RetrievalConcept-based RetrievalCoverageDocument RetrievalMedical Document RetrievalPagerankQuery-document RelevanceRelevance Assessment
We describe our participation in the task 3 of ShARe/CLEF eHealth Lab 2013: information retrieval to address questions patient may have when reading clinical reports. In our experiments, we focus mainly on two levels of analysis, namely query analysis and document analysis, to disclose the relevance between query and documents. In terms of query analysis, we first observe each medical-oriented query to find its identical or related UMLS concepts derived from the query, which may help to induce relevant results that refer to the same thing but are represented in other surface forms. In such manner, we extend the query based on the medical concepts so as to achieve a bigger coverage. In terms of document analysis, we leverage different scores (e.g., relevance score, PageRank score, HITS score and layout score) as feature to re-rank the documents of search results. With those two levels of analysis, we implement a concept-based method and a topic-based method to accomplish the task of medical document retrieval. Experiments indicate that the proposed method is effective.
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Jon Kleinberg · Journal of the ACM · 1999 · 9.1K citations · Full text