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Users' Manual and Validation of the Automated Grading System (AGS): Improving the Quality of Intelligence Summaries Using Feedback from an Unsupervised Model of Semantics
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In: DTIC (2012)
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Enhancing a Web Crawler with Arabic Search Capability
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In: DTIC (2010)
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A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
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In: DTIC (2009)
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Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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| 5 |
A Journey in Entity Related Retrieval for TREC 2009
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In: DTIC (2009)
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| 7 |
Lucene for n-grams using the ClueWeb Collection
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In: DTIC (2009)
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| 8 |
BIT at TREC 2009 Faceted Blog Distillation Task
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In: DTIC (2009)
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| 9 |
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
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In: DTIC (2009)
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| 10 |
POSTECH at TREC 2009 Blog Track: Top Stories Identification
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In: DTIC (2009)
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| 11 |
Entity Retrieval by Hierarchical Relevance Model, Exploiting the Structure of Tables and Learning Homepage Classifiers
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In: DTIC (2009)
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| 12 |
Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia
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In: DTIC (2009)
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| 13 |
PRIS at 2009 Relevance Feedback track: Experiments in Language Model for Relevance Feedback
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In: DTIC (2009)
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| 14 |
A Study of Faceted Blog Distillation -- PRIS at TREC 2009 Blog Track
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In: DTIC (2009)
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| 15 |
Experiments on Related Entity Finding Track at TREC 2009
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In: DTIC (2009)
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| 16 |
THUIR at TREC 2009 Web Track: Finding Relevant and Diverse Results for Large Scale Web Search
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Li, Z. C.; Chen, F.; Xing, Q. L.; Miao, J. W.; Xue, Y. F.; Zhu, T.; Zhou, B.; Chen, R. W.; Liu, Y. Q.; Zhang, M.; Jin, Y.; Ma, S. P.
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In: DTIC (2009)
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Abstract:
This is the 8th year that IR group of Tsinghua University (THUIR) participates in TREC. This year we focus on Web track, which contains two tasks, namely ad hoc and diversity. On ad hoc task, we improved the efficiency of our distributed retrieval system TMiner to handle terabytes of Web data. Then three studies have been done, namely page quality estimation, ranking feature analysis, and model comparison. On diversity task, we proposed several new approaches on searching strategy, user intention detection, and duplication elimination. To mine user's intention, we proposed and compared two different strategies, namely 'searching + content-based diversity' which is a kind of result clustering, and 'user based diverse intention prediction + searching' which is in the branch of query expansion. ; Published in Proceedings of the Text REtrieval Conference (18th), TREC 2009, held in Gaithersburg, MD, 17-20 Nov 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.
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Keyword:
*INFORMATION RETRIEVAL; *INTERNET BROWSERS; ALGORITHMS; ANTI-SPAM TECHNIQUES; CHINA; CLUSTERING; Computer Programming and Software; Cybernetics; FOREIGN REPORTS; HIERARCHICAL INCREMENTAL ONLINE CLUSTERING ALGORITHM; Information Science; LARGE SCALE WEB SEARCHES; Linguistics; METADATA; MODEL COMPARISON; MODELS; PAGE QUALITY ESTIMATION; RANKING; RANKING FEATURE ANALYSIS; SEARCHING; SYMPOSIA; TEXT PROCESSING; TEXT RETRIEVAL; TITLE KEYWORD STUFFING
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URL: http://www.dtic.mil/docs/citations/ADA517807
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517807
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| 17 |
PARADISE Based Search Engine at TREC 2009 Web Track
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In: DTIC (2009)
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| 18 |
Microsoft Research at TREC 2009. Web and Relevance Feedback Tracks
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In: DTIC (2009)
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| 19 |
FEUP at TREC 2009 Blog Track: Temporal Evidence in the Faceted Blog Distillation Task
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In: DTIC (2009)
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| 20 |
Enhancing P3P Framework through Policies and Trust
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In: DTIC AND NTIS (2005)
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