Cengage Learning at TREC 2011 Medical Track

Abstract

This paper details Cengage Learning’s submissions for this year’s TREC medical track. The techniques we used fall roughly into two categories: information extraction and query expansion. From both the queries and the medical reports, we extracted limiting attributes, such as age, race, and gender, and labeled terms appearing in the Unified Medical Language System (UMLS). We also used three different techniques of query expansion: UMLS related terms, terms from a network built from UMLS, and terms from our medical reference encyclopedias. We submitted four different runs varying only in their methods of query expansion.