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Biological Information Processing in Single Microtubules
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In: DTIC (2014)
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| 2 |
Using Target Network Modelling to Increase Battlespace Agility
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In: DTIC (2013)
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| 3 |
Enabling Efficient Intelligence Analysis in Degraded Environments
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In: DTIC (2013)
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| 4 |
An Approach Using MIP Products for the Development of the Coalition Battle Management Language Standard
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In: DTIC (2013)
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| 6 |
Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
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In: DTIC (2013)
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| 7 |
Interacting with Multi-Robot Systems Using BML
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In: DTIC (2013)
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| 8 |
QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents
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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
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In: DTIC (2012)
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| 10 |
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
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In: DTIC (2012)
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| 11 |
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
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In: DTIC (2011)
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| 12 |
An Intelligence Process Model Based on a Collaborative Approach
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In: DTIC (2011)
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| 13 |
A Smarter Common Operational Picture: The Application of Abstraction Hierarchies to Naval Command and Control
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In: DTIC (2011)
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| 14 |
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
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In: DTIC (2011)
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| 15 |
Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
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In: DTIC (2011)
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| 16 |
Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
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In: DTIC (2010)
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| 18 |
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
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In: DTIC (2009)
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Abstract:
This paper addresses the problem of related entity finding, which was proposed in trec 2009. The overall aim of related entity finding (REF) is to perform entity-related search on Web data, which address common information needs that are not that well modeled as ad hoc document search. In this paper, a novel framework was proposed based on a probabilistic model for related entity finding in a Web collection. This model consists of two parts. One is the probability indicating the relation between the source entity and the candidate entities. The other is the probability indicating the relevance between the candidate entities and the topic. Using ClueWeb09 dataset, the experimental evaluations show the effectiveness of our REF framework. ; 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).
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Keyword:
*INFORMATION RETRIEVAL; *INFORMATION SCIENCES; *INTERNET; *REF(RELATED ENTITY FINDING); *WEB(INTERNET); CHINA; COLLECTION; Computer Programming and Software; Equipment and Methods; FOREIGN REPORTS; ICTNET(INSTITUTE OF COMPUTING TECHNOLOGY NETWORK); Information Science; INTERNET BROWSERS; Linguistics; MATHEMATICAL MODELS; PROBABILITY; QUERIES(INFORMATION RETRIEVAL); RELEVANCE(INFORMATION RETRIEVAL); REPRINTS; SOCIAL COMMUNICATION; SYMPOSIA; TEST AND EVALUATION; Test Facilities
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URL: http://www.dtic.mil/docs/citations/ADA517725
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517725
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| 19 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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