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The broad goal of information extraction is to derive structured information from unstructured data.
Text mining for product attribute extraction
R. Ghani, K. Probst, Y. Liu, M. Krema, and A. Fano · 1931
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Building a large annotated corpus of english: The penn treebank
M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini · 1993
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Bootstrapped named entity recognition for product attribute extraction
D. P. Putthividhya and J. Hu · 2011
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Unsupervised extraction of popular product attributes from web sites
L. Bing, T.-L. Wong, and W. Lam · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Unsupervised extraction of attributes and their values from product description
K. Shinzato and S. Sekine · 2013
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Learning image embeddings using convolutional neural networks for improved multi-modal semantics
D. Kiela and L. Bottou · 2014
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Convolutional neural networks for sentence classification
Y. Kim · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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The Stanford CoreNLP natural language processing toolkit
C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky · 2014
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
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Very deep convolutional networks for large-scale image recognition
Combining language and vision with a multimodal skip-gram model
A. Lazaridou, N. T. Pham, and M. Baroni · 2015
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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Y. Zhang and B. Wallace · 2015
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Attribute extraction from product titles in ecommerce
A. More · 2016
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Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
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K. Simonyan and A. Zisserman · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
P. Young, A. Lai, M. Hodosh, and J. Hockenmaier · 2014
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VQA: Visual Question Answering
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Bottom-up and top-down attention for image captioning and vqa
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang · 2017
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Gated multimodal units for information fusion
J. Arevalo, T. Solorio, M. Montes-y Gómez, and F. A. González · 2017
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