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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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Abstract:
The University of Waterloo participated in the Related Entity Finding task of the Entity track. Our goal is to investigate whether related entity finding problem can be addressed by unsupervised approaches that rely primarily on statistical methods and common linguistic tools, such as named-entity taggers and syntactic parsers. We approach the related entity finding problem by first retrieving documents in response to the query, and extracting an initial set of candidate entities from the text of the documents. As a separate step, we automatically construct a set of seed entities, which represent hyponyms of the target entity category specified in the narrative, and then rank the candidate entities by their similarity to the seeds. An example of the target entity category name is "authors", extracted from the narrative "Authors awarded an Anthony Award at Bouchercon in 2007" (2009 topic #14). The system extracts category names from the free-text narrative, finds seed entities belonging to each category, and computes the similarity of candidate entities to the seeds. ; Presented at the Text REtrieval Conference (TREC 2010) (19th) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text Retrieval Conference (TREC 2010) (19th), 2010. Sponsored in part 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; CANADA; ENTITIES; EXTRACTION; FOREIGN REPORTS; Information Science; LINGUISTICS; RANKING; RELATED ENTITY FINDING; STATISTICAL PROCESSES; SYMPOSIA
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA546752
http://www.dtic.mil/docs/citations/ADA546752
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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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| 19 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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