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1
Multiple retrieval models and regression models for prior art search
In: http://clef.isti.cnr.it/2009/working_notes/lopez-paperCLEF2009.pdf (2009)
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2
Multiple retrieval models and regression models for prior art search
In: http://hal.archives-ouvertes.fr/docs/00/41/18/35/PDF/technote.pdf (2009)
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3
F.Llopis. AliQAn and BRILI QA systems at CLEF 2006
In: http://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-FerrandezEt2006.pdf (2006)
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4
Extraction of Definitions for Bulgarian
In: http://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-Tanev2006.pdf
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5
Evaluating Answer Validation in Spanish Question Answering
In: http://ceur-ws.org/Vol-1174/CLEF2008wn-QACLEF-TellezValero2008.pdf
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6
Statistical vs. Rule-Based Stemming for Monolingual French
In: http://ceur-ws.org/Vol-1172/CLEF2006wn-adhoc-MajumderEt2006.pdf
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7
Evaluating Answer Validation in Spanish Question Answering
In: http://ccc.inaoep.mx/~mmontesg/publicaciones/2008/UsingAVinQA-CLEF08.pdf
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8
XRCE’s Participation to CLEF 2008 Ad-Hoc Track
In: http://clef.isti.cnr.it/2008/working_notes/clinchant-paperCLEF2008.pdf
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9
Robust Question Answering for Speech Transcripts: UPC Experience in QAst 2009
In: http://clef.isti.cnr.it/2009/working_notes/comas-paperCLEF2009.pdf
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10
Benefits of deep NLP-based lemmatization for information retrieval
In: http://ceur-ws.org/Vol-1172/CLEF2006wn-adhoc-Halacsy2006.pdf
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11
Amharic-English Information Retrieval
In: http://clef.isti.cnr.it/2006/working_notes/workingnotes2006/atelachalemuargawCLEF2006.pdf
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12
Categories and Subject Descriptors
In: http://ceur-ws.org/Vol-1173/CLEF2007wn-WebCLEF-Adafre2007.pdf
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13
LIG at ImageCLEFphoto 2008
In: http://clef.isti.cnr.it/2008/working_notes/mulhem-paperCLEF2008.pdf
Abstract: This working notes describe the runs and results obtained by the LIG at ImageCLEF-photo 2008. The submitted runs are: two runs (text only and text+image) without diversification on classes, and two runs (text only and text+image) with class diversi-fication were submitted. The text retrieval is based on language model of Information Retrieval, and the image part is processed using RGB histograms on 9 image blocks with a similarity value based on Jeffrey divergence. Results using text+image are obtained by a linear combination of normalized results on text and image. The diver-sification is based on clusters, according to the cluster given in the queries. When the cluster name is not directly extracted from the images (like city or country), we apply a visual clustering. Not surprisingly, the cluster recall at 20 (i.e., cr(20)) results are higher for the runs that include diversification. On the other hand, the precision at 20 and the mean average precision results are higher without diversification on our runs, for both text only and image+text results.
Keyword: Categories and Subject Descriptors H.3 [Information Storage and Retrieval; Experimentation Keywords Text; H.2.3 [Database Managment; H.3.1 Content Analysis and Indexing; H.3.3 Infor- mation Search and Retrieval; H.3.4 Systems and Software; H.3.7 Digital Libraries; Languages—Query Languages General Terms Measurement; Performance
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.7813
http://clef.isti.cnr.it/2008/working_notes/mulhem-paperCLEF2008.pdf
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14
de Rijke. Overview of WiQA 2006
In: http://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-JijkounEt2006.pdf
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15
CUHK Experiments with ImageCLEF 2005 ∗
In: http://clef.isti.cnr.it/2005/working_notes/workingnotes2005/hoi05.pdf
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