KDEIR at CLEF eHealth 2016: Health Documents Re-ranking Based on Query Variations

For the plain entity recognition task, five teams submitted a total of 9 runs for each of the corpora, EMEA and MEDLINE (18 runs in total).. For the normalized entity recognition

Key words: Named-Entity Recognition, Automatic Text Categorization, Discon- tinuous Entity Extraction, Relocation, Statistical Training, Concept Normalization,

In the first ap- proach, a feedback model is constructed using new relevance scores using the word vectors while it is constructed with a new query expanded..

Keywords: Health Information Retrieval, Query Expansion, Medical Text Indexer, Pseudo Relevance Feedback, Wikipedia, UMLS, Latent Dirichlet Allocation, Link Analysis, Readability..

In detail, for each query, we use synonyms and hypernyms extracted from UMLS to produce alternative formulations (example in Table 1); then, we re- trieve results for each

In 2016 CLEF eHealth task 3, We propose a Web-based query expansion model and a combination method to achieve the better performance for medical IR. In the future, we will continue

To help laypeople, Conference and Labs of the Evaluation Forum (CLEF) launched the eHealth Evaluation Lab in 2013[1].The 2016 CLEF eHealth Task 3 Patient-Centred Information

We applied the proposed approach with the optimal weights obtained from TREC 2011 and 2012 Medical Records Track into the Query Likelihood model (Run 2) and Markov Random Field