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Kommunikation in der naturwissenschaftlichen Forschung: eine Studie aus sozialpsychologisch-semiotischer Perspektive
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In: Journal für Psychologie ; 6 ; 2 ; 53-69 (2012)
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| 42 |
Lexical Link Analysis for the Haiti Earthquake Relief Operation Using Open Data Sources
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In: DTIC (2011)
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| 43 |
Incremental Syntactic Language Models for Phrase-Based Translation
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In: DTIC (2011)
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| 44 |
Feasibility of Leveraging Crowd Sourcing for the Creation of a Large Scale Annotated Resource for Hindi English Code Switched Data: A Pilot Annotation
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In: DTIC (2011)
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| 45 |
Segregation of Whispered Speech Interleaved with Noise or Speech Maskers
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In: DTIC (2011)
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| 47 |
The SRI NIST 2010 Speaker Recognition Evaluation System (PREPRINT)
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In: DTIC (2011)
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| 48 |
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
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In: DTIC (2011)
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| 49 |
An Intelligence Process Model Based on a Collaborative Approach
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In: DTIC (2011)
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| 50 |
Entity List Completion Using Set Expansion Techniques
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In: DTIC (2011)
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| 51 |
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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| 52 |
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
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In: DTIC (2011)
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| 53 |
Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
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In: DTIC (2011)
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| 54 |
Maritime Domain Awareness via Agent Learning and Collaboration
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In: DTIC (2010)
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| 55 |
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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| 56 |
Projecting Grammatical Features in Nominals: Cognitive Processing Theory & Computational Implementation
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In: DTIC (2010)
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| 57 |
The Impact of Interpretation Problems on Tutorial Dialogue
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In: DTIC (2010)
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| 58 |
A Multi-Disciplinary University Research Initiative in Hard and Soft Information Fusion: Overview, Research Strategies and Initial Results
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In: DTIC (2010)
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| 59 |
Enabling Multinational Communications with CENTRIXS
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In: DTIC (2010)
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| 60 |
Towards Real-time Program Awareness via Lexical Link Analysis
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In: DTIC (2010)
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