title: Differentiating Sense through Semantic Interaction Data.
abstract: Words which have different representations but are semantically related, such as dementia and delirium, can pose difficult issues in understanding text. We explore the use of interaction frequency data between semantic elements as a means to differentiate concept pairs, using semantic predications extracted from the biomedical literature. We applied datasets of features drawn from semantic predications for semantically related pairs to two Expectation Maximization clustering processes (without, and with concept labels), then used all data to train and evaluate several concept classifying algorithms. For the unlabeled datasets, 80% displayed expected cluster count and similar or matching proportions; all labeled data exhibited similar or matching proportions when restricting cluster count to unique labels. The highest performing classifier achieved 89% accuracy, with F1 scores for individual concept classification ranging from 0.69 to 1. We conclude with a discussion on how these findings may be applied to natural language processing of clinical text.
authors.affiliation: VA Salt Lake City Health Care, Salt Lake City, Utah; Division of Epidemiology, University of Utah, Salt Lake City, UT.
authors.forename: T
authors.initials: T
authors.lastname: Elizabeth Workman
authors.affiliation: VA Salt Lake City Health Care, Salt Lake City, Utah; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
authors.forename: Charlene
authors.initials: C
authors.lastname: Weir
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D012984:Software; D009626:Terminology as Topic