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TIACRITIS System and Textbook: Learning Intelligence Analysis through Practice
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In: DTIC (2010)
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| 63 |
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
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In: DTIC (2009)
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| 64 |
Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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| 65 |
Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
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In: DTIC (2009)
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| 66 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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| 68 |
A Journey in Entity Related Retrieval for TREC 2009
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In: DTIC (2009)
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| 70 |
Lucene for n-grams using the ClueWeb Collection
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In: DTIC (2009)
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| 71 |
BIT at TREC 2009 Faceted Blog Distillation Task
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In: DTIC (2009)
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| 72 |
Recovering Asynchronous Watermark Tones from Speech
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In: DTIC (2009)
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| 73 |
Sparse Matrix Factorization: Applications to Latent Semantic Indexing
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In: DTIC (2009)
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| 74 |
IRRA at TREC 2009: Index Term Weighting based on Divergence From Independence Model
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In: DTIC (2009)
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| 76 |
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
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In: DTIC (2009)
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| 77 |
POSTECH at TREC 2009 Blog Track: Top Stories Identification
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In: DTIC (2009)
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| 78 |
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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Abstract:
This paper gives an overview of our work done for the TREC 2009 Entity track. We propose a hierarchical relevance retrieval model for entity ranking. In this model, three levels of relevance are examined which are document, passage and entity, respectively. The final ranking score is a linear combination of the relevance scores from the three levels. Furthermore, we exploit the structure of tables and lists to identify the target entities from them by making a joint decision on all the entities with the same attribute. To find entity homepages, we train logistic regression models for each type of entities. A set of templates and filtering rules are also used to identify target entities. The key lessons that we learned by participating this year's Entity track include: 1) our special treatment of table and list data is well rewarding; 2) The high accuracy of homepage finding is crucial in this track; 3) Wikipedia can serve as a valuable knowledge resource for different aspects of the related entity finding task. ; 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:
*HOMEPAGES(INTERNET); *INFORMATION RETRIEVAL; *INFORMATION SCIENCES; *INTERNET; *RELEVANCE(INFORMATION RETRIEVAL); *WEB(INTERNET); ACCURACY; CLASSIFICATION; Computer Programming and Software; DECISION MAKING; DOCUMENT RETRIEVAL; ENTITY EXTRACTION; ENTITY RANKING; ENTITY TRACK; Equipment and Methods; FILTERS; HIERARCHIES; HIGH RATE; Information Science; INTERNET BROWSERS; LEARNING; Linguistics; MODELS; RANKING; REGRESSION ANALYSIS; REPRINTS; SOCIAL COMMUNICATION; SYMPOSIA; TABLES(DATA); TEMPLATES; Test Facilities; WIKIPEDIA(INTERNET)
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517741
http://www.dtic.mil/docs/citations/ADA517741
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| 79 |
University of Padua at TREC 2009: Relevance Feedback Track
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In: DTIC (2009)
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| 80 |
Related Entity Finding Based on Co-Occurrence
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In: DTIC (2009)
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