Stephanie W. Haas

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OBJECTIVES Emergency Medical Text Processor (EMT-P) version 1, a natural language processing system that cleans emergency department text (e.g., chst pn, chest pai), was developed to maximize extraction of standard terms (e.g., chest pain). The authors compared the number of standard terms extracted from raw chief complaint (CC) data with that for CC data(More)
The Constituent Object Parser is a shallow syntactic parser designed to produce dependency tree representations of syntactic structure that can be used to specify the intended meanings of a sentence more precisely than can the key terms of the sentence alone. It is intended to improve the precision/recall performance of information retrieval and similar(More)
As government agencies provide increasing amounts of information through their Web sites, more people are attempting to make sense of it. The result is a significant volume of e-mail queries - many of which boil down to "Where can I find X?" or "What does X mean exactly?" Such queries underline a major stumbling block to widespread digital access: how best(More)
Emergency Department (ED) data are a key component of bioterrorism surveillance systems. Little research has been done to examine differences in ED data capture and entry across hospitals, regions and states. The purpose of this study was to describe the current state of ED data for use in bioterrorism surveillance in 2 regions of the country. We found that(More)
The Case Hierarchy model describes the case system of unconstrained natural language and the ways in which the case system is specialized in a restricted domain. It is the basis of a procedure for the analysis of such restricted domains. The resulting representations can be used to determine the case related requirements of a case grammar based natural(More)
The use of terms from natural and social scientific titles and abstracts is studied from the perspective of sublanguages and their specialized dictionaries. Different notions of sublanguage distinctiveness are explored. Objective methods for separating hard and soft sciences are suggested based on measures of sublanguage use, dictionary characteristics, and(More)