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Abstract Meaning Representation (AMR) Annotation Release 3.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
BASE
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2
Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020
BASE
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3
Abstract Meaning Representation (AMR) Annotation Release 2.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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4
Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017
BASE
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5
Abstract Meaning Representation (AMR) Annotation Release 1.0
Knight, Kevin; Baranescu, Laura; Bonial, Claire. - : Linguistic Data Consortium, 2014. : https://www.ldc.upenn.edu, 2014
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6
HyTER Networks of Selected OpenMT08/09 Sentences
Dreyer, Markus; Marcu, Daniel. - : Linguistic Data Consortium, 2014. : https://www.ldc.upenn.edu, 2014
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7
HyTER Networks of Selected OpenMT08/09 Sentences ...
Dreyer, Markus; Marcu, Daniel. - : Linguistic Data Consortium, 2014
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8
Abstract Meaning Representation (AMR) Annotation Release 1.0 ...
Palmer, Martha; Marcu, Daniel; Griffitt, Kira. - : Linguistic Data Consortium, 2014
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9
Hierarchical search for word alignment
In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 157-166
BLLDB
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10
Induction of Word and Phrase Alignments for Automatic Document Summarization ...
Daumé, Hal; Marcu, Daniel. - : arXiv, 2009
Abstract: Current research in automatic single document summarization is dominated by two effective, yet naive approaches: summarization by sentence extraction, and headline generation via bag-of-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they produce summaries. One possible explanation for the widespread use of these models is that good techniques have been developed to extract appropriate training data for them from existing document/abstract and document/headline corpora. We believe that future progress in automatic summarization will be driven both by the development of more sophisticated, linguistically informed models, as well as a more effective leveraging of document/abstract corpora. In order to open the doors to simultaneously achieving both of these goals, we have developed techniques for automatically producing word-to-word and phrase-to-phrase alignments between documents and their ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.0907.0804
https://arxiv.org/abs/0907.0804
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11
Measuring word alignment quality for statistical machine translation
In: Computational linguistics. - Cambridge, Mass. : MIT Press 33 (2007) 3, 293-303
BLLDB
OLC Linguistik
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12
ISI Chinese-English Automatically Extracted Parallel Text
Dragos Stefan Munteanu; Marcu, Daniel. - : Linguistic Data Consortium, 2007. : https://www.ldc.upenn.edu, 2007
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13
ISI Arabic-English Automatically Extracted Parallel Text
Dragos Stefan Munteanu; Marcu, Daniel. - : Linguistic Data Consortium, 2007. : https://www.ldc.upenn.edu, 2007
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14
ISI Chinese-English Automatically Extracted Parallel Text ...
Dragos Stefan Munteanu; Marcu, Daniel. - : Linguistic Data Consortium, 2007
BASE
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15
ISI Arabic-English Automatically Extracted Parallel Text ...
Dragos Stefan Munteanu; Marcu, Daniel. - : Linguistic Data Consortium, 2007
BASE
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16
Scalable Inference and Training of Context-Rich Syntactic Translation Models
Graehl, Jonathan; Galley, Michel; DeNeefe, Steve. - : Proceedings of COLING/ACL, 2006
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17
Scalable Inference and Training of Context-Rich Syntactic Translation Models ...
Galley, Michel; Graehl, Jonathan; Knight, Kevin. - : Columbia University, 2006
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18
Improving machine translation performance by exploiting non-parallel corpora
In: Computational linguistics. - Cambridge, Mass. : MIT Press 31 (2005) 4, 477-504
BLLDB
OLC Linguistik
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19
Induction of word and phrase alignments for automatic document summarization
In: Computational linguistics. - Cambridge, Mass. : MIT Press 31 (2005) 4, 505-530
BLLDB
OLC Linguistik
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20
What's in a Translation Rule
In: DTIC (2004)
BASE
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