Abstract
In this paper, we present our participation in CLEF Consumer Health Search Task 2018, mainly, its monolingual and multilingual subtasks: IRTask1 and IRTask4. In IRTask1, we use language-model based retrieval model, vector-space model and Kullback-Leiber divergence query expansion mechanism to build our runs.
In IRTask4, we submitted 4 runs for each language of Czech, French and German. We follow query-translation approach in which we employ a Statistical Machine Translation (SMT) system to get a ranked list of translation hypotheses in English.
We use this list for two systems: the first one uses 1-best-list translation to construct queries, and the second one uses a hypotheses reranker to select the best translation (in terms of retrieval performance) to construct queries. We also present our term reranking model for query expansion, in which we deploy feature set from different resources (the document collection, Wikipedia articles, translation hypotheses).
These features are used to train a logisti