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Hits 81 – 100 of 200

81
The MIT-LL/AFRL IWSLT-2006 MT System
In: DTIC (2006)
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82
Reading to Learn
In: DTIC (2006)
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83
Transformative Pattern Learning
In: DTIC (2006)
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84
Trainable Videorealistic Speech Animation
In: DTIC (2006)
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85
Towards Collaboration with Robots in Shared Space: Spatial Perspective and Frames of Reference
In: DTIC (2005)
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86
A Note on Topical N-Grams
In: DTIC (2005)
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87
Human Language Technology: Opportunities and Challenges
In: DTIC (2005)
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88
Intelligence Revisited
In: DTIC (2005)
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89
Una aproximación multilingüe a la clasificación de preguntas basada en aprendizaje automático
Tomás, David; Vicedo, Jose-Luis; Suárez Cueto, Armando. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2005
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90
Automatic text categorisation of racist webpages
Greevy, Edel. - : Dublin City University. School of Computing, 2004
In: Greevy, Edel (2004) Automatic text categorisation of racist webpages. Master of Science thesis, Dublin City University. (2004)
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91
Analysis of Statistical Question Classification for Fact-based Questions
In: http://ciir.cs.umass.edu/pubfiles/ir-323.pdf (2004)
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92
Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis
In: DTIC (2004)
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93
Semantic Role Labeling via Integer Linear Programming Inference
In: DTIC (2004)
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94
Learning to Identify TV News Monologues by Style and Context
In: DTIC (2003)
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95
Linguistic Resource Creation for Research and Technology Development: A Recent Experiment
In: DTIC (2003)
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96
Word-level Alignment for Multilingual Resource Acquisition
In: DTIC (2002)
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97
Early Pragmatic Language Development for an Infant Robot
In: DTIC (2002)
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98
On Communicating with Semantic Machines
In: DTIC (2002)
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99
Trainable Videorealistic Speech Animation
In: DTIC (2002)
Abstract: I describe how to create with machine learning techniques a generative, video realistic, speech animation module. A human subject is first recorded using a video camera as he/she utters a pre-determined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth littering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is video- realistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned. ; The original document contains color images.
Keyword: *ANIMATIONS; *LEARNING MACHINES; *SPEECH; *VIDEO RECORDING; *VIDEO SIGNALS; *VIDEOREALISTIC SPEECH; BACKGROUND; CAMERAS; EYE MOVEMENTS; HUMANS; INPUT; LEARNING; METHODOLOGY; MMM(MULTIDIMENSIONAL MORPHABLE MODEL); MODULAR CONSTRUCTION; MOUTH; OUTPUT; Radio Communications; Recording and Playback Devices; SEQUENCES; SOUND; TEXT TO SPEECH SYSTEMS; TRAJECTORY SYNTHESIS MODULES; VISION
URL: http://www.dtic.mil/docs/citations/ADA456049
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA456049
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100
Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning
In: DTIC AND NTIS (2002)
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