Stephen Helmreich

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While relational databases have become critically important in business applications and web services, they have played a relatively minor role in scientific computing, which has generally been concerned with modeling and simulation activities. However, massively parallel database architectures are beginning to offer the ability to quickly search through(More)
Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can(More)
Since MT systems, whatever translation method they employ, do not reach an optimum output on free text; each method handles some problems better than others. The PANGLOSS Mark III system is an MT environment that uses the best results from a variety of independent MT systems or engines working simultaneously within a single framework on the same text. This(More)
An unprecedented combination of simula-tive and metaphor based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs productively uses conceptual metaphors such as MIND AS CONTAINER and IDEAS AS INTERNAL UTTERANCES, and ATT-Meta's metaphor-based reasoning accordingly leads to crucial discourse comprehension(More)
Machine Readable Dictionaries (MRDs) contain much useful information about lan£uage. Researchers have worked for the last decade on ways to extract this information for language processing systems. But processing dictionaries for use in natural language computation is itself a difficult problem. Transforming information from a version designed for human(More)
An unprecedented combination of simulative and metaphor-based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs uses conceptual metaphors (e.g., MIND AS CONTAINER) productively, and ATT-Meta's metaphor-based reasoning accordingly leads to crucial discourse comprehension decisions. ATT-Meta's non-metaphorical(More)
PURPOSE Willingness and ability to learn from health information in text are crucial for people to be informed and make better medical decisions. These two user characteristics are influenced by the perceived and actual difficulty of text. Our goal is to find text features that are indicative of perceived and actual difficulty so that barriers to reading(More)
This paper focuses on an important step in the creation of a system of meaning representation and the development of semantically-annotated parallel corpora, for use in applications such as machine translation, question answering, text summarization, and information retrieval. The work described below constitutes the first effort of any kind to annotate(More)
We de ne within-vehicle and within-tenor reasoning to be reasoning that is done on-they within the vehicle domain or tenor domain, respectively, of a conceptual metaphor, during the comprehension of utterances that manifest the metaphor. The main claim of this paper is that, at least in Arti cial Intelligence systems for understanding metaphorical(More)