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1
Strengthening Homeland Security through Improved Foreign Language Capability
In: DTIC (2011)
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2
Special Operations Forces Language and Culture Needs Assessment: Defense Language Proficiency Test (DLPT)
In: DTIC (2010)
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3
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
In: DTIC (2009)
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4
BIT at TREC 2009 Faceted Blog Distillation Task
In: DTIC (2009)
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5
POSTECH at TREC 2009 Blog Track: Top Stories Identification
In: DTIC (2009)
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6
PRIS at 2009 Relevance Feedback track: Experiments in Language Model for Relevance Feedback
In: DTIC (2009)
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7
Minimum Bayes-Risk Decoding for Statistical Machine Translation
In: DTIC (2004)
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8
The Pragmatics of Taking a Spoken Language System Out of the Laboratory
In: DTIC (2003)
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9
Uses of the Diagnostic Rhyme Test (English Version) for Predicting the Effects of Communicators' Linguistic Backgrounds on Voice Communications in English: An Exploratory Study
In: DTIC (2000)
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10
The Bible, Truth, and Multilingual OCR Evaluation
In: DTIC (1998)
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11
Experiments in Spoken Document Retrieval at CMU
In: DTIC (1997)
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12
Domain and Language Evaluation Results
In: DTIC (1993)
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13
AMAR: A Computational Model of Autosegmental Phonology
In: DTIC AND NTIS (1993)
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14
A Practical Methodology for the Evaluation of Spoken Language Systems
In: DTIC (1992)
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15
Subject-Based Evaluation Measures for Interactive Spoken Language Systems
In: DTIC (1992)
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16
VHDL and Waves Descriptions for a Pseudo-Random Pattern Generator
In: DTIC AND NTIS (1992)
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17
Robust Processing of Real-World Natural-Language Texts
In: DTIC AND NTIS (1991)
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18
The Semantic Analyzer.
In: DTIC AND NTIS (1991)
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19
Very High Speed Integrated Circuits (VHSIC) Hardware Description Language (VHDL) Syntax and Semantics Summary
In: DTIC AND NTIS (1991)
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20
Adaptive Natural Language Processing
In: DTIC AND NTIS (1991)
Abstract: A handful of special purpose systems have been successfully deployed to extract prespecified kinds of data from text. The limitation to widespread deployment of such systems is their assumption of a large volume of handcrafted, domain-dependent, and language-dependent knowledge in the form of rules. A new approach is to add automatically trainable probabilistic language models to linguistically based analysis. This offers several potential advantages: (1) Trainability by finding patterns in a large corpus, rather than handcrafting such patterns. (2) Improvability be re-estimating probabilities based on a user marking correct and incorrect output on a test set. (3) More accurate selection among interpretations when more than one is produced. (4) Robustness by finding the most likely partial interpretation when no complete interpretation can be found.
Keyword: *INFORMATION PROCESSING; *MESSAGE PROCESSING; *NATURAL LANGUAGE; *VOLUME; ACCURACY; ADAPTIVE SYSTEMS; CLASSIFICATION; INFORMATION PROCESSING; Information Science; LANGUAGE; Linguistics; MARKERS; MODELS; NATURAL LANGUAGE; PARSERS; PATTERNS; PE61101E; PILOT STUDIES; PROBABILITY; SELECTION; SEMANTICS; TEST SETS; TEXT; TRAINING; USER NEEDS; WURLG302QA01
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA241336
http://www.dtic.mil/docs/citations/ADA241336
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