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A Hybrid Approach to Clinical Question Answering
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In: DTIC (2014)
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| 2 |
Towards a Simple and Efficient Web Search Framework
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In: DTIC (2014)
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| 4 |
Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014
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In: DTIC (2014)
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| 5 |
Modelling Psychological Needs for User-dependent Contextual Suggestion
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In: DTIC (2014)
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| 6 |
Making Semantic Information Work Effectively for Degraded Environments
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In: DTIC (2013)
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| 7 |
Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media
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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 |
Adding a Capability to Extract Sentiment from Text Using HanDles
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In: DTIC (2012)
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| 10 |
Speaker Clustering for a Mixture of Singing and Reading (Preprint)
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In: DTIC (2012)
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| 11 |
Horizontal Integration of Warfighter Intelligence Data: A Shared Semantic Resource for the Intelligence Community
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In: DTIC (2012)
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| 12 |
SAWUS: Siena's Automatic Wikipedia Update System
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In: DTIC (2012)
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| 13 |
CSSG: Learning within NLP Pipelines for Scalable Data Mining and Information Extraction
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In: DTIC (2011)
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| 14 |
Entity List Completion Using Set Expansion Techniques
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In: DTIC (2011)
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| 15 |
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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| 16 |
Enhancing a Web Crawler with Arabic Search Capability
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In: DTIC (2010)
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| 17 |
Extrinsic Evaluation of Automated Information Extraction Programs
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In: DTIC (2010)
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| 19 |
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
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In: DTIC (2009)
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| 20 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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