Integration of Low Level Linguistic Information for Clinical Document Semantic Tagging

Abstract

We propose a semantic tagger that provides high level concept information for phrases based on several kinds of low level information about words in clinical narrative texts. The semantic tagging, based on hidden Markov model (HMM), is performed on the text that has been tagged with unified medical language system (UMLS), part-of-speech (POS), and abbreviation tags. It reuses UMLS, POS, abbreviation, clue words, and numerical information to produce higher level concept information. Our unknown phrase guessing method for a robust tagger also uses the existing information calculated in the training data. In short, the semantic tagger gives more meaningful and abstract information by integrating different kinds of low-level information