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M.: Application of Axiomatic Approaches to Crosslanguage Retrieval
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In: http://clef.isti.cnr.it/2009/working_notes/rkern-paperCLEF2009.pdf (2010)
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Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
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In: http://clef.isti.cnr.it/2009/working_notes/larsonVideoCLEF2009_overview.pdf (2009)
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Multiple retrieval models and regression models for prior art search
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In: http://clef.isti.cnr.it/2009/working_notes/lopez-paperCLEF2009.pdf (2009)
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Multiple retrieval models and regression models for prior art search
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In: http://hal.archives-ouvertes.fr/docs/00/41/18/35/PDF/technote.pdf (2009)
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German, French, English and Persian Retrieval Experiments at CLEF 2009
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In: http://clef.isti.cnr.it/2009/working_notes/tomlinson-paperCLEF2009.pdf (2009)
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G.: IXA at CLEF 2008 Robust-WSD Task: using Word Sense Disambiguation for (Cross Lingual) Information Retrieval
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In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1226564935/publikoak/otegi-paperCLEF2008.pdf (2009)
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Overview of the ImageCLEFphoto 2008 Photographic Retrieval Task. 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access (CLEF 2008
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In: http://clef.isti.cnr.it/2008/working_notes/ImageCLEFphoto2008-final.pdf (2008)
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Back to Basics - Again - for Domain Specific Retrieval. This volume
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In: http://clef.isti.cnr.it/2008/working_notes/Berkeley_Domain_Specific_08.pdf (2008)
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German, French, English and Persian Retrieval Experiments at CLEF 2008
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In: http://clef.isti.cnr.it/2008/working_notes/tomlinson-paperCLEF2008.pdf (2008)
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Cross Lingual Question Answering using QRISTAL for CLEF 2007
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In: http://www.qristal.fr/pub/Cross Lingual Question Answering using QRISTAL for CLEF 2007.pdf (2007)
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L.: The University of West Bohemia at CLEF 2006, the CL-SR track
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In: http://ceur-ws.org/Vol-1172/CLEF2006wn-CLSR-IrcingEt2006.pdf (2006)
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de Rijke. Overview of WebCLEF 2006
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In: http://ceur-ws.org/Vol-1172/CLEF2006wn-WebCLEF-BalogEt2006a.pdf (2006)
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F.Llopis. AliQAn and BRILI QA systems at CLEF 2006
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In: http://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-FerrandezEt2006.pdf (2006)
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Overview of the CLEF 2005 Multilingual Question Answering Track
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In: http://www.clef-campaign.org/2005/working_notes/workingnotes2005/vallin05.pdf (2005)
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Overview of the CLEF 2005 Multilingual Question Answering Track
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In: http://www.science.uva.nl/~mdr/Publications/Files/clef-2005-qa-overview-wn.pdf (2005)
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Sanchis: TPIRS: A System for Document Indexing Reduction on WebCLEF, Extended abstract in Working notes of CLEF’05, Viena
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In: http://clef.isti.cnr.it/2005/working_notes/workingnotes2005/pinto05.pdf (2005)
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Extraction of Definitions for Bulgarian
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In: http://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-Tanev2006.pdf
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Evaluating Answer Validation in Spanish Question Answering
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In: http://ceur-ws.org/Vol-1174/CLEF2008wn-QACLEF-TellezValero2008.pdf
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Prior Art Search using International Patent Classification Codes and All-Claims-Queries
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In: http://atlas.tk.informatik.tu-darmstadt.de/Publications/2009/CLEF2009-IP_final.pdf.pdf
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Evaluating Answer Validation in Spanish Question Answering
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In: http://ccc.inaoep.mx/~mmontesg/publicaciones/2008/UsingAVinQA-CLEF08.pdf
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Abstract:
This paper introduces the new INAOE’s answer validation method. This method is based on supervised learning approach that uses a set of attributes that capture some lexical-syntactic relations among the question, the answer and the given support text. In addition, the paper describes the evaluation of the proposed method at both the Spanish Answer validation Exercise (AVE 2008) and the Spanish Question Answering Main Task (QA 2008). The evaluation objectives were twofold. One the one hand, evaluate the ability of our answer validation method to discriminate correct from in-correct answers, and on the other hand, measure the impact of including an answer validation module in our QA system. The evaluation results were encouraging; the proposed method achieved a 0.39 F-measure in the detection of correct answers, out-performing the baseline result of the AVE 2008 task by more than 100%. It also enhanced the performance of our QA system, showing a gain in accuracy of 22 % for answering factoid questions. Furthermore, when there were evaluated three candidate answers per question, the answer validation method allowed increasing the MRR of our QA system by 40%, reaching a MRR of 0.28.
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Keyword:
Answer Validation; Categories and Subject Descriptors H.3 [Information Storage and Retrieval; Experimentation Keywords Question Answering; 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; Machine Learning; Performance; Textual Entailment
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URL: http://ccc.inaoep.mx/~mmontesg/publicaciones/2008/UsingAVinQA-CLEF08.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.4021
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