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1
Biological Information Processing in Single Microtubules
In: DTIC (2014)
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2
Using Target Network Modelling to Increase Battlespace Agility
In: DTIC (2013)
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3
Enabling Efficient Intelligence Analysis in Degraded Environments
In: DTIC (2013)
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4
An Approach Using MIP Products for the Development of the Coalition Battle Management Language Standard
In: DTIC (2013)
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5
C2-Simulation Interoperability in NATO
In: DTIC (2013)
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6
Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
In: DTIC (2013)
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7
Interacting with Multi-Robot Systems Using BML
In: DTIC (2013)
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8
QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents
In: DTIC (2012)
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9
Conference Report: Cultural and Linguistic Advancement for Mission Success: Enhancing Language, Regional and Cultural Capabilities Across Whole of Government for an Effective COIN Strategy
In: DTIC (2012)
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10
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
In: DTIC (2012)
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11
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
In: DTIC (2011)
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12
An Intelligence Process Model Based on a Collaborative Approach
In: DTIC (2011)
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13
A Smarter Common Operational Picture: The Application of Abstraction Hierarchies to Naval Command and Control
In: DTIC (2011)
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14
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
In: DTIC (2011)
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15
Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
In: DTIC (2011)
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16
Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
In: DTIC (2010)
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17
RMIT University at TREC 2009: Web Track
In: DTIC (2009)
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18
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
In: DTIC (2009)
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19
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
In: DTIC (2009)
Abstract: We introduce our participation of the TREC Relevance Feedback(RF) TRACK in 2009. The RF09 TRACK is focused on the explicit relevant feedback, where a few relevant and irrelevant documents are available to each query. Our system is implemented under the framework of probabilistic language model. We apply the constrained clustering on the top returned documents and extract the expanded words to reform the query. We also extract the named entities from the explicit relevant documents to expand the query. The experiment was conducted on the ClueWeb09 TREC Category B, which is a new and huge test collection for the TREC TRACKs. The evaluation result shows the performance of the constrained clustering. ; Presented at the Text REtrieval Conference (TREC 2009, 18th) held in Gaithersburg, Maryland on 17-20 November 2009. Published in the Proceedings of the Text REtrieval Conference (TREC 2009, 18th), 2009. The conference was co-sponsored by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA). The original document contains color images.
Keyword: *CLUSTERING; *FEEDBACK; *INFORMATION RETRIEVAL; *INFORMATION SCIENCES; *INTERNET; *RF(RELEVANCE FEEDBACK); CHINA; COLLECTION; Computer Programming and Software; CONSTRAINED CLUSTERING; Equipment and Methods; EXPANSION; FOREIGN REPORTS; Information Science; INTERNET BROWSERS; LANGUAGE; Linguistics; MATHEMATICAL MODELS; PROBABILITY; QUERIES(INFORMATION SCIENCES); RELEVANCE(INFORMATION SCIENCES); REPRINTS; SOCIAL COMMUNICATION; SYMPOSIA; TEST AND EVALUATION; Test Facilities; WORDS(LANGUAGE)
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517715
http://www.dtic.mil/docs/citations/ADA517715
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20
ICTNET at Web Track 2009 Diversity task
In: DTIC (2009)
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