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State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer.
Interaction with texts: Information retrieval as information-seeking behavior
Nicholas Belkin. 1993 · 1993
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Falcon: Boosting knowledge for answer engines
Sanda Harabagiu, Dan Moldovan, Marius Pasca, Rada Mihalcea, Mihai Surdeanu, Razvan Bunescu, Roxana Girju, Vasile Rus, and Paul Morarescu. 2000 · 2000
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An analysis of the AskMSR question-answering system
Eric Brill, Susan Dumais, and Michele Banko. 2002 · 2002
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Performance issues and error analysis in an open-domain question answering system
Dan Moldovan, Marius Paşca, Sanda Harabagiu, and Mihai Surdeanu. 2003 · 2003
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Dan Shen and Dietrich Klakow. 2006 · 2006
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Aqualog: An ontology-driven question answering system for organizational semantic intranets
Vanessa Lopez, Victoria Uren, Enrico Motta, and Michele Pasin. 2007 · 2007
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Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Simple and effective multi-paragraph reading comprehension
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Bernhard Kratzwald and Stefan Feuerriegel. 2018 · 2018
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Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016 · 2016
Cited alongside, same era.
R3: Reinforced ranker-reader for open-domain question answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, and Jing Jiang. 2018 · 2018
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