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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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Event-based summarization using a centrality-as-relevance model
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A logical representation of Arabic questions toward automatic passage extraction from the Web
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In: ISSN: 1381-2416 ; EISSN: 1572-8110 ; International Journal of Speech Technology ; https://hal.archives-ouvertes.fr/hal-01794688 ; International Journal of Speech Technology, Springer Verlag, 2017, 20 (2), pp.339 - 353. ⟨10.1007/s10772-017-9411-7⟩ (2017)
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.sorbonne-universite.fr/hal-01156600 ; BMC Bioinformatics, BioMed Central, 2015, 16 (1), pp.138. ⟨10.1186/s12859-015-0564-6⟩ (2015)
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LIMSI-CNRS@ CLEF 2015: Tree Edit Beam Search for Multiple Choice Question Answering.
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In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum ; CLEF 2015 ; https://hal.archives-ouvertes.fr/hal-02289246 ; CLEF 2015, Sep 2015, Toulouse, France ; http://ceur-ws.org/Vol-1391/ (2015)
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LIMSI-CNRS@ CLEF 2014: Invalidating Answers for Multiple Choice Question Answering.
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In: Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014 ; CLEF 2014 ; https://hal.archives-ouvertes.fr/hal-02290008 ; CLEF 2014, Sep 2014, Sheffield, United Kingdom. pp.1386--1394 (2014)
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SNUMedinfo at CLEFeHealth2013 task 3
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In: http://ceur-ws.org/Vol-1179/CLEF2013wn-CLEFeHealth-ChoiEt2013.pdf (2013)
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Voice-QA: Evaluating the Impact of Misrecognized Words on Passage Retrieval
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In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; Advances in Artificial Intelligence - IBERAMIA 2012 ; 13th Ibero-American Conference on AI ; https://hal.archives-ouvertes.fr/hal-00825246 ; 13th Ibero-American Conference on AI, Nov 2012, Cartagena de Indias, Colombia. pp.462-471 (2012)
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Finding answers to questions, in text collections or web, in open domain or specialty domains
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In: Next Generation Search Engines: Advanced Models for Information Retrieval ; https://hal.archives-ouvertes.fr/hal-02289728 ; Jouis, Christophe AND Biskri, Ismail AND Ganascia, Jean-Gabriel AND Roux, Magali. Next Generation Search Engines: Advanced Models for Information Retrieval, IGI Global, pp.344--370, 2012 (2012)
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| 11 |
Voice-QA: evaluating the impact of misrecognized words on passage retrieval
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DCU at the NTCIR-9 spokendoc passage retrieval task
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In: Eskevich, Maria orcid:0000-0002-1242-0753 and Jones, Gareth J.F. orcid:0000-0003-2923-8365 (2011) DCU at the NTCIR-9 spokendoc passage retrieval task. In: The 9th NTCIR Workshop Meeting, 6-9 Dec 2011, Tokyo, Japan. ISBN 978-4-86049-056-0 (2011)
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Recuperación de pasajes multilingüe para la búsqueda de respuestas ; Multilingue passage retrieval for question answering
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Gómez, José M.. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2008
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| 14 |
Question Analysis and Answer Passage Retrieval for Opinion Question Answering Systems
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In: http://www.aclclp.org.tw/clclp/v13n3/v13n3a3.pdf (2007)
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Abstract:
Question answering systems provide an elegant way for people to access an underlying knowledge base. However, people are interested in not only factual questions, but also opinions. This paper deals with question analysis and answer passage retrieval in opinion QA systems. For question analysis, six opinion question types are defined. A two-layered framework utilizing two question type classifiers is proposed. Algorithms for these two classifiers are described. The performance achieves 87.8 % in general question classification and 92.5 % in opinion question classification. The question focus is detected to form a query for the information retrieval system and the question polarity is detected to retain relevant sentences which have the same polarity as the question. For answer passage retrieval, three components are introduced. Relevant sentences retrieved are further identified as to whether the focus (Focus Detection) is in a scope of opinion (Opinion Scope Identification) or not, and, if yes, whether the polarity of the scope and the polarity of the question (Polarity Detection) match with each other. The best model achieves an F-measure of 40.59 % by adopting partial match for relevance detection at the level of meaningful unit. With relevance issues removed, the F-measure of the best model boosts up to 84.96%.
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Keyword:
Answer Passage Retrieval; Opinion Extraction; Question Answering; Question Type
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.360.6619
http://www.aclclp.org.tw/clclp/v13n3/v13n3a3.pdf
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Scoring missing terms in information retrieval tasks
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In: http://hachita.nmsu.edu/ref/Terra-cikm04-MissingTermsIR.pdf (2004)
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Measurement, Experimentation
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In: http://www.cs.otago.ac.nz/sigirfocus/paper_13.pdf
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A Semantic Approach to Boost Passage Retrieval Effectiveness for Question Answering
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In: http://crpit.com/confpapers/CRPITV48Ofoghi.pdf
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