Indexing UMLS Semantic Types for Medical Question-Answering

Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose semantic types which generally cover names of persons, organizations and locations, dates and amounts, etc. If we are to build medical QA systems, a set of medically relevant named entities must be used. In this paper, we explore the use of the UMLS (Unified Medical Language System) Semantic Network semantic types for this purpose. We present an experiment where the French part of the UMLS Metathesaurus, together with the associated semantic types, is used as a resource for a medically-specific named entity tagger. We also explore the detection of Semantic Network relations for answering specific types of medical questions. We present results and evaluations on a corpus of French-language medical documents that was used in the EQueR Question-Answering evaluation forum. We show, using statistical studies, that strategies for using these new tags in a QA context are to take in account the individual origin of documents.