Association measures for estimating semantic similarity and relatedness between biomedical concepts

Association measures quantify the observed likelihood a term pair co-occurs versus their predicted co-occurrence together if by chance. This is based both on the terms' individual occurrence frequencies, and their mutual co-occurrence frequencies. One application of association scores is estimating semantic relatedness, which is critical for many natural language processing applications, such as clustering of biomedical and clinical documents and the development of biomedical terminologies and ontololgies. In this paper we propose a method of generating association scores between biomedical concepts to estimate semantic relatedness. We use co-occurrence statistics between Unified Medical Language System (UMLS) concepts to account for lexical variation at the synonymous level, and introduce a process of concept expansion that exploits hierarchical information from the UMLS to account for lexical variation at the hyponymous level. State of the art results are achieved on several standard evaluation datasets, and an in depth analysis of hyper-parameters is presented.