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Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.
Cyc: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks
Douglas Lenat, Mayank Prakash, and Mary Shepherd · 1986
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
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Overview of the tac 2010 knowledge base population track
Heng Ji, Ralph Grishman, Hoa Trang Dang, Kira Griffitt, and Joe Ellis · 2010
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Learning structured embeddings of knowledge bases
Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio · 2011
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Local and global algorithms for disambiguation to wikipedia
Lev Ratinov, Dan Roth, Doug Downey, and Mike Anderson · 2011
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
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Fine-grained entity recognition
Xiao Ling and Daniel S Weld · 2012
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Relational inference for wikification
Xiao Cheng and Dan Roth · 2013
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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Learning word embeddings efficiently with noise-contrastive estimation
Andriy Mnih and Koray Kavukcuoglu · 2013
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Relation extraction with matrix factorization and universal schemas
Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin · 2013
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Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng · 2013
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Entity hierarchy embedding
Zhiting Hu, Poyao Huang, Yuntian Deng, Yingkai Gao, and Eric Xing · 2015
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Two/too simple adaptations of word2vec for syntax problems
Wang Ling, Chris Dyer, Alan W Black, and Isabel Trancoso · 2015
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Personalized page rank for named entity disambiguation
Maria Pershina, Yifan He, and Ralph Grishman · 2015
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Modeling mention, context and entity with neural networks for entity disambiguation
Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, and Xiaolong Wang · 2015
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Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon · 2015
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Corpus-level fine-grained entity typing using contextual information
Yadollah Yaghoobzadeh and Hinrich Schütze · 2015
World knowledge for reading comprehension: Rare entity prediction with hierarchical lstms using external descriptions
Teng Long, Emmanuel Bengio, Ryan Lowe, Jackie Chi Kit Cheung, and Doina Precup · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning distributed representations of texts and entities from knowledge base
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji · 2017
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Ultra-fine entity typing
Eunsol Choi, Omer Levy, Yejin Choi, and Luke Zettlemoyer · 2018
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End-to-end neural entity linking
Nikolaos Kolitsas, Octavian-Eugen Ganea, and Thomas Hofmann · 2018
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Hierarchical losses and new resources for fine-grained entity typing and linking
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Evaluating induced ccg parsers on grounded semantic parsing
Yonatan Bisk, Siva Reddy, John Blitzer, Julia Hockenmaier, and Mark Steedman · 2016
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Leveraging lexical resources for learning entity embeddings in multi-relational data
Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, and Doina Precup · 2016
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Who did what: A large-scale person-centered cloze dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester · 2016
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Compositional learning of embeddings for relation paths in knowledge base and text
Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon, and Chris Quirk · 2016
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Joint learning of the embedding of words and entities for named entity disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji · 2016
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Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, and Andrew McCallum · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Time Salimans, and Ilya Sutskever · 2018
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ELDEN: Improved entity linking using densified knowledge graphs
Priya Radhakrishnan, Partha Talukdar, and Vasudeva Varma · 2018
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DeepType: Multilingual entity linking by neural type system evolution
Jonathan Raiman and Olivier Raiman · 2018
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Neural Cross-Lingual entity linking
Avirup Sil, Gourab Kundu, Radu Florian, and Wael Hamza · 2018
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Probabilistic embedding of knowledge graphs with box lattice measures
Luke Vilnis, Xiang Li, Shikhar Murty, and Andrew McCallum · 2018
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Corpus-level fine-grained entity typing
Yadollah Yaghoobzadeh, Heike Adel, and Hinrich Schütze · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova · 2019
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