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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Impact of Machine-Translated Text on Entity and Relationship Extraction Mittrick, Mark R; Richardson, John T In: DTIC (2014) BASE Show details 2 Effectively Using Syntax for Recognizing False Entailment Snow, Rion; Vanderwende, Lucy; Menezes, Arul In: DTIC (2006) BASE Show details 3 A Hybrid Approach for QA Track Definitional Questions Blair-Goldensohn, Sasha; McKeown, Kathleen R.; Schlaikjer, Andrew H. In: DTIC (2006) BASE Show details 4 Building Effective Queries in Natural Language Information Retrieval Strzalkowski, Tomek; Lin, Fang; Perez-Carballo, Jose... In: DTIC (1997) BASE Show details 5 UMass/Hughes: Description of the Circus System Used for MUC-5 Lehnert, W.; McCarthy, J.; Soderland, S.; Riloff, E.; Cardie, C.; Peterson, J.; Feng, F.; Dolan, C.; Goldman, S. 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 BASE Hide details 6 GE-CMU: Description of the Shogun System Used for MUC-5 Jacobs, Paul S.; Krupka, George; Rau, Lisa... In: DTIC (1993) BASE Show details 7 Corpora and Data Preparation Carlson, Lynn; Onyshkevych, Boyan; Okurowski, Mary E. In: DTIC (1993) BASE Show details 8 Tipster Shogun System (Joint GE-CMU): MUC-4 Test Results and Analysis Krupka, George; Jaco, Paul; Mauldin, Michael... In: DTIC (1992) BASE Show details 9 Military Specification Job Performance Aids, Advanced-Type, for VNAF Organizational Maintenance. In: DTIC AND NTIS (1970) BASE Show details 10 DYNAMIC ADAPTIVE DATA BASE MANAGEMENT STUDY Lehmann, Winfred P.; Pendergraft, Eugene D. In: DTIC AND NTIS (1967) BASE Show details

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