A Study on Query Expansion with MeSH Terms and Elasticsearch. IMS Unipd at CLEF eHealth Task 3

We presented our approach for query expansion, which includes concept can- didates generation from Wikipedia and DBpedia, and the selection of K-best concepts according to the

EN_BM25_Phrases(6)_title: phrases, IPC, title, cutoff = 6 terms In Table 2, the results of the post runs are provided.. Therefore, we might summarize that longer phrases tend

Given this distribution of categories, we merged the training and the development set into one dataset that we used to train the classifiers with a k-fold cross validation..

This task focuses on the prob- lem of medical systematic reviews, a problem which requires a recall close (if not equal) to 100%. Semi-Automated approaches are essential to sup-

In particular, we were interested in automatically gathering medical acronyms from a Wikipedia page and manually cleaning the table of expanded acronyms (for example,

– ims iafa m10k150f0m10, run-1, this run uses Interactive Automatic Feed- back with query Aspects (iafa) and, for each topic, uses k = 150 feedback rounds where, at each round, a

Based on the above considerations, we’ve designed a medical information retrieval framework which is characterized with relevance feedback for initial search and query expansion

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