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Compound probabilistic context-free grammars (C-PCFGs) have recently established a new state of the art for unsupervised phrase-structure grammar induction.
An efficient recognition and syntax-analysis algorithm for context-free languages
Tadao Kasami. 1966 · 1966
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Recognition and parsing of context-free languages in time n3
Daniel H. Younger. 1967 · 1967
Earlier work this paper cites.
Programming Languages and Their Compilers: Preliminary Notes
John Cocke. 1969 · 1969
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Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977 · 1977
Earlier work this paper cites.
The estimation of stochastic context-free grammars using the inside-outside algorithm
K. Lari and S.J. Young. 1990 · 1990
Earlier work this paper cites.
The Penn Treebank: Annotating predicate argument structure
Mitchell Marcus, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, and Britta Schasberger. 1994 · 1994
Cited alongside, same era.
Treebank-3
Mitchell P Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz, and Ann Taylor. 1999 · 1999
Cited alongside, same era.
The penn chinese treebank: Phrase structure annotation of a large corpus
Naiwen Xue, Fei Xia, Fu-dong Chiou, and Marta Palmer. 2005 · 2005
Cited alongside, same era.
English web treebank
Ann Bies, Justin Mott, Colin Warner, and Seth Kulick. 2012 · 2012
Cited alongside, same era.
Introducing the SPMRL 2014 shared task on parsing morphologically-rich languages
Djamé Seddah, Sandra Kübler, and Reut Tsarfaty. 2014 · 2014
Later among the works it cites.
Compound probabilistic context-free grammars for grammar induction
Yoon Kim, Chris Dyer, and Alexander Rush. 2019 · 2019
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Torch-struct: Deep structured prediction library
Alexander Rush. 2020 · 2020
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Visually grounded compound PCFGs
Yanpeng Zhao and Ivan Titov. 2020 · 2020
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