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We describe work aimed at building commonsense knowledge by reading word definitions using deep understanding techniques. The end result is a knowledge base allowing complex concepts to be reasoned about using OWL-DL reasoners. We show that we can use this system to automatically create a mid-level ontology for WordNet verbs that has good agreement with(More)
In this paper, we present our ongoing work towards an OWL-based framework for extracting a variety of information (including patient history) from clinical texts. Our framework integrates a well-known natural language processing (NLP) system by converting its ontology and output logical form interpretation into the Web Ontology Language (OWL). The OWL-based(More)
An innovative task learning system called PLOW (Procedure Learning On the Web) lets end-users teach procedural tasks to automate their various web activities. Deep natural understanding and mixed-initiative interaction in PLOW makes the teaching process very natural and intuitive while producing efficient/workable procedures.
In this article, we describe the TEGUS system for mining geospatial path data from natural language descriptions. TEGUS uses natural language processing and geospa-tial databases to recover path coordinates from user descriptions of paths at street level. We also describe the PURSUIT Corpus — an annotated corpus of geospatial path descriptions in spoken(More)
A novel 'play-by-play' based procedure learning system has been successfully applied to automate Web tasks. In the transition to a non-Web domain, for a system widely used at US military hospitals, we addressed new challenges by enabling the system to learn collabo-rative tasks and understand unstructured application environment. This paper presents our(More)
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