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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 Page: 1 2 3 4 5 6 7 8 9 10 Hits 81 – 100 of 200 81 The MIT-LL/AFRL IWSLT-2006 MT System Shen, Wade; Delaney, Brian; Anderson, Tim In: DTIC (2006) BASE Show details 82 Reading to Learn Israel, David J.; Clark, Peter E.; Harrison, Phil... In: DTIC (2006) BASE Show details 83 Transformative Pattern Learning Jensen, David In: DTIC (2006) BASE Show details 84 Trainable Videorealistic Speech Animation Ezzat, Tony; Geiger, Gadi; Poggio, Tomaso In: DTIC (2006) BASE Show details 85 Towards Collaboration with Robots in Shared Space: Spatial Perspective and Frames of Reference Schultz, Alan C.; Trafton, J. G. In: DTIC (2005) BASE Show details 86 A Note on Topical N-Grams Wang, Xuerui; McCallum, Andrew In: DTIC (2005) BASE Show details 87 Human Language Technology: Opportunities and Challenges Ostendorf, Mari; Shriberg, Elizabeth; Stolcke, Andreas In: DTIC (2005) BASE Show details 88 Intelligence Revisited Everett, H. R. In: DTIC (2005) BASE Show details 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 BASE Show details 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) BASE Show details 91 Analysis of Statistical Question Classification for Fact-based Questions Donald Metzler; W. Bruce Croft In: http://ciir.cs.umass.edu/pubfiles/ir-323.pdf (2004) BASE Show details 92 Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis Ando, Rie K. In: DTIC (2004) BASE Show details 93 Semantic Role Labeling via Integer Linear Programming Inference Punyakanok, Vasin; Roth, Dan; Yih, Wen-tau... In: DTIC (2004) BASE Show details 94 Learning to Identify TV News Monologues by Style and Context Snoek, Cees G.; Hauptmann, Alexander G. In: DTIC (2003) BASE Show details 95 Linguistic Resource Creation for Research and Technology Development: A Recent Experiment Strassel, Stephanie; Maxwell, Mike; Cieri, Christopher In: DTIC (2003) BASE Show details 96 Word-level Alignment for Multilingual Resource Acquisition Lopez, Adam; Nossal, Michael; Hwa, Rebecca... In: DTIC (2002) BASE Show details 97 Early Pragmatic Language Development for an Infant Robot Varchavskaia, Paulina In: DTIC (2002) BASE Show details 98 On Communicating with Semantic Machines Freeman, Walter J. In: DTIC (2002) BASE Show details 99 Trainable Videorealistic Speech Animation Ezzat, Tony F. In: DTIC (2002) BASE Show details 100 Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning Li, Eliot; Nicholas, Charles; Oates, Tim; Mehra, Raman K. In: DTIC AND NTIS (2002) Abstract: The objective of this STTR project is to develop an information management system to rapidly and accurately linking records of related information from web-based information sources. The sheer magnitude of information available online via the Internet has overwhelmed the ability of existing search tools to produce useful query responses. Current web-search techniques typically fail to correlate relevant documents that are identified in different ways, such as synonyms and acronyms (aliases). The challenge is to find an approach that can obtain highly accurate matches even when those documents do not share any obvious attributes with the query, and with minimal information requirement from the user. Latent Semantic Analysis (LSA) is a technique for identifying both semantically similar words and semantically similar documents. On the face of it, LSA should work well for the task of discovering aliases. That is, for a given word we can use LSA to produce a rank-ordered list of words that are semantically similar to it and aliases for the name should be high in this list. In this Phase I, we tested this conjecture empirically and found, surprisingly, that under a broad range of circumstances a straightforward application of LSA fails to rank the aliases highly. We then developed a two-stage algorithm that takes the output of LSA, creates a new set of pseudo-documents, and runs LSA again on these new documents. Empirical results show that this two-stage algorithm performs remarkably well in identifying aliases, even in those cases for which a single application of LSA fails miserably. University of Maryland (Baltimore County) is the research institute partner for this effort, under the direction of Professor Charles Nicholas and Tim Oates. ; Prepared in cooperation with Univ. of Maryland, Baltimore County, Baltimore, MD. Keyword: *COMPUTATIONAL LINGUISTICS; Computer Programming and Software; Cybernetics; INFORMATION RETRIEVAL; Information Science; KNOWLEDGE MANAGEMENT; LEARNING MACHINES; Linguistics; NATURAL LANGUAGE; SEMANTICS; STTR(SMALL BUSINESS TECHNOLOGY TRANSFER); TEXT PROCESSING; WORD RECOGNITION URL: http://www.dtic.mil/docs/citations/ADA408937 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA408937 BASE Hide details Page: 1 2 3 4 5 6 7 8 9 10

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