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1
Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses
In: DTIC (2013)
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2
Integrating Hard and Soft Information Sources for D2D Using Controlled Natural Language
In: DTIC (2012)
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3
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
In: DTIC (2011)
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4
Recognizing Connotative Meaning in Military Chat Communications
In: DTIC (2009)
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5
UDEL/SMU at TREC 2009 Entity Track
In: DTIC (2009)
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6
Deep Versus Broad Methods for Automatic Extraction of Intelligence Information From Text
In: DTIC (2005)
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7
Robustness Versus Fidelity in Natural Language Understanding
In: DTIC (2004)
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8
HITIQA: A Data Driven Approach to Interactive Analytical Question Answering
In: DTIC (2004)
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9
Utterance Classification in Auto Tutor
In: DTIC (2003)
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10
HITIQA: An Interactive Question Answering System. A Preliminary Report
In: DTIC (2003)
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11
Inducing Multilingual Text Analysis Tools via Robust Projection across Aligned Corpora
In: DTIC (2001)
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12
Converting Dependency Structures to Phrase Structures
In: DTIC (2001)
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13
Facilitating Treebank Annotation Using a Statistical Parser
In: DTIC (2001)
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14
UMass/Hughes: Description of the Circus System Used for MUC-5
In: DTIC (1993)
Abstract: The primary goal of our effort is the development of robust and portable language processing capabilities for information extraction applications. The system under evaluation here is based on language processing components that have demonstrated strong performance capabilities in previous evaluations [Lehnert et al. 1992a]. Having demonstrated the general viability of these techniques, we are now concentrating on the practicality of our technology by creating trainable system components to replace hand-coded data and manually-engineered software. Our general strategy is to automate the construction of domain-specific dictionaries and other language- related resources so that information extraction can be customized for specific applications with a minimal amount of human assistance. We employ a hybrid system architecture that combines selective concept extraction [Lehnert 1991] technologies developed at UMass with trainable classifier technologies developed at Hughes [Dolan et al. 1991]. Our MUC-5 system incorporates seven trainable language components to handle (1) lexical recognition and part-of-speech tagging, (2) knowledge of semantic/syntactic interactions, (3) semantic feature Lagging, (4) noun phrase analysis, (5) limited conference resolution, (6) domain object recognition, and (7) relational link recognition. Our trainable components have been developed so domain experts who have no background in natural language or machine learning can train individual system components in the space of a few hours.
Keyword: *INFORMATION RETRIEVAL; *NATURAL LANGUAGE; COMPUTER ARCHITECTURE; EXTRACTION; HYBRID SYSTEMS; Information Science; INTERACTIONS; LANGUAGE; LANGUAGE PROCESSING; LEARNING MACHINES; LEXICOGRAPHY; Linguistics; PERFORMANCE(ENGINEERING); PHRASE STRUCTURE GRAMMARS; PORTABLE EQUIPMENT; PROCESSING; PROCESSING EQUIPMENT; RECOGNITION; SEMANTICS; STRATEGY; SYMPOSIA; SYNTAX; VIABILITY
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458576
http://www.dtic.mil/docs/citations/ADA458576
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15
GE-CMU: Description of the Shogun System Used for MUC-5
In: DTIC (1993)
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16
A Simple Rule-Based Part of Speech Tagger
In: DTIC (1992)
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17
BBN PLUM: MUC-3 Test Results and Analysis
In: DTIC (1991)
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18
Elements of a Computational Model of Cooperative Response Generation
In: DTIC (1989)
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19
The Rate of Progress in Natural Language Processing
In: DTIC (1987)
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20
Transportability and Generality in a Natural-Language Interface System
In: DTIC (1983)
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