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Abstract: Even quite sophisticated users can experience difficulty navigating large collections of data to locate the answers to their queries. We describe AskCal, a system that employs natural language processing, an ontology, a query planner, and various feedback mechanisms to assist a user in refining his or her query and then in executing and visualizing it. We trace several interactions with AskCal in the domain of energy time series, and show how a combination of modalities, including ATN parsing... (Update)
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BibTeX entry: (Update)
@misc{ time-dgrc,
author = "For Energy Time",
title = "DGRC AskCal: Natural Language Question Answering",
url = "citeseer.ist.psu.edu/720998.html" }
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