Lexical Substitution for the Medical Domain
Martin Johannes Riedl, Michael Glass, Alfio Gliozzo · 2014
In this paper we examine the lexical substitu-tion task for the medical domain. We adapt the current best system from the open domain, which trains a single classifier for all instances using delexicalized features. We show sig-nificant improvements over a strong baseline coming from a distributional thesaurus (DT). Whereas in the open domain system, features derived from WordNet show only slight im-provements, we show that its counterpart for the medical domain (UMLS) shows a signif-icant additional benefit when used for feature generation. 1