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This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few hand-crafted features.
Freebase: a collaboratively created graph database for structuring human knowledge
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor · 2008
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Large scale image annotation: learning to rank with joint word-image embeddings
J. Weston, S. Bengio, and N. Usunier · 2010
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A survey on question answering technology from an information retrieval perspective
O. Kolomiyets and M.-F. Moens · 2011
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Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Ré, S. J. Wright, and F. Niu · 2011
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Entity linking at web scale
T. Lin, O. Etzioni, et al · 2012
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Template-based question answering over rdf data
C. Unger, L. Bühmann, J. Lehmann, A.-C. Ngonga Ngomo, D. Gerber, and P. Cimiano · 2012
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Semantic parsing on Freebase from question-answer pairs
J. Berant, A. Chou, R. Frostig, and P. Liang · 2013
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Paraphrase-driven learning for open question answering
A. Fader, L. Zettlemoyer, and O. Etzioni · 2013
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Scaling semantic parsers with on-the-fly ontology matching
T. Kwiatkowski, E. Choi, Y. Artzi, and L. Zettlemoyer · 2013
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Semantic parsing via paraphrasing
J. Berant and P. Liang · 2014
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Question answering with subgraph embeddings
A. Bordes, S. Chopra, and J. Weston · 2014
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Open question answering with weakly supervised embedding models
A. Bordes, J. Weston, and N. Usunier · 2014
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Open question answering over curated and extracted knowledge bases
A. Fader, L. Zettlemoyer, and O. Etzioni · 2014
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Information extraction over structured data: Question answering with freebase
X. Yao and B. Van Durme · 2014
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