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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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| 3 |
Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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| 4 |
Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
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In: DTIC (2009)
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| 5 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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| 7 |
A Journey in Entity Related Retrieval for TREC 2009
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In: DTIC (2009)
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| 9 |
Lucene for n-grams using the ClueWeb Collection
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In: DTIC (2009)
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| 10 |
BIT at TREC 2009 Faceted Blog Distillation Task
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In: DTIC (2009)
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| 11 |
Recovering Asynchronous Watermark Tones from Speech
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In: DTIC (2009)
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| 12 |
Sparse Matrix Factorization: Applications to Latent Semantic Indexing
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In: DTIC (2009)
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| 13 |
IRRA at TREC 2009: Index Term Weighting based on Divergence From Independence Model
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In: DTIC (2009)
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| 15 |
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
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In: DTIC (2009)
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| 16 |
POSTECH at TREC 2009 Blog Track: Top Stories Identification
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In: DTIC (2009)
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| 17 |
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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| 18 |
University of Padua at TREC 2009: Relevance Feedback Track
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In: DTIC (2009)
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Abstract:
In the Relevance Feedback (RF) task the user is directly involved in the search process: given an initial set of results, he specifies if they are relevant or not to the achievement of his information goal. In the TREC 2009 RF track the first five documents retrieved by the baseline systems were judged by the assessors and then used as evidence for the RF algorithms to be tested. The specific algorithm we tested is mainly based on a geometric framework which allows the latent semantic associations of terms in the feedback documents to be modeled as a vector subspace; the documents of the collection represented as vectors of TFIDF weights were re-ranked according to their distance from the subspace. The adopted geometric framework was used in past works as a basis for Implicit Relevance Feedback (IRF) and Pseudo Relevance Feedback (PRF) algorithms; the participation to the RF track allows us to make some preliminary investigations on the effectiveness of the adopted framework when it is exploited to support explicit RF on much larger test collections, thus complementing the work carried out for the other RF strategies. ; Published in the Proceedings of the Eighteenth Text REtrieval Conference (TREC 2009) held in Gaithersburg, Maryland, November 17-20, 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:
*ALGORITHMS; *FEEDBACK; *INFORMATION RETRIEVAL; *SEMANTICS; BASE LINES; COLLECTION; DOCUMENTS; FOREIGN REPORTS; Information Science; ITALY; Linguistics; RF(RELEVANT FEEDBACK); SEARCHING; STRATEGY; SYMPOSIA; TEST AND EVALUATION
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517683
http://www.dtic.mil/docs/citations/ADA517683
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| 19 |
Related Entity Finding Based on Co-Occurrence
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In: DTIC (2009)
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| 20 |
Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia
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In: DTIC (2009)
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