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1
Text rewriting with missing supervision
Gildea, Daniel J.; Riley, Parker. - : University of Rochester, 2021
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2
Generalized Shortest-Paths Encoders for AMR-to-Text Generation ...
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3
Unsupervised Bilingual Lexicon Induction Across Writing Systems ...
Riley, Parker; Gildea, Daniel. - : arXiv, 2020
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4
Semantic Neural Machine Translation Using AMR
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 19-31 (2019) (2019)
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5
Multi-rate HMMs for Word Alignment ...
Eyigoz, Elif; Gildea, Daniel; Oflazer, Kemal. - : Carnegie Mellon University, 2018
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6
Multi-rate HMMs for Word Alignment ...
Eyigoz, Elif; Gildea, Daniel; Oflazer, Kemal. - : Carnegie Mellon University, 2018
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7
N-ary Relation Extraction using Graph State LSTM ...
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8
Addressing the Data Sparsity Issue in Neural AMR Parsing ...
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9
Sense Embedding Learning for Word Sense Induction ...
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10
Unsupervised alignment of natural language with video
Naim, Iftekhar; Gildea, Daniel J.. - : University of Rochester, 2016
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11
Human languages order information efficiently ...
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12
Morphology modeling for statistical machine translation
Eyigoz, Kadriye Elif (1977 - ); Gildea, Daniel J.; Oflazer, Kemal. - : University of Rochester, 2014
Abstract: Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2014. ; Word-alignment is the initial step in most state-of-the art approaches to statistical machine translation. Morphologically rich languages pose problems for current statistical machine translation systems, including word-alignment, the most common problem being data sparsity. Current word-alignment models do not take into account morphology beyond merely treating morphemes as words. We present a new word alignment model that distinguishes between words and morphemes. Our model does not collapse words and morphemes into one single category, therefore we can legitimately talk about words and their morphemes in line with the linguistic conception of these terms. We adopt a two-level representation of alignment: the first level involves word alignment, the second level involves morpheme alignment in the scope of a given word alignment. Two-level alignment models (TAM) can align rarely occurring words through their frequently occurring morphemes. Our model induces word and morpheme alignments jointly using the expectation maximization algorithm. We present the HMM extension of TAM, which is an instance of a multi-scale HMM. The two-level HMM we present addresses reordering between morpheme positions and word positions simultaneously.
Keyword: Machine translation; Morphology; Statistical machine translation; Word alignment
URL: http://hdl.handle.net/1802/28475
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13
Bayesian tree substitution grammars as a usage-based approach
In: Language and speech. - London [u.a.] : Sage Publ. 56 (2013) 3, 291-308
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OLC Linguistik
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14
Simultaneous Word-Morpheme Alignment for Statistical Machine Translation ...
Eyigoz, Elif; Gildea, Daniel; Oflazer, Kemal. - : Carnegie Mellon University, 2013
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15
Simultaneous Word-Morpheme Alignment for Statistical Machine Translation ...
Eyigoz, Elif; Gildea, Daniel; Oflazer, Kemal. - : Carnegie Mellon University, 2013
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16
Using latent information for natural language processing tasks
Chung, Tagyoung; Gildea, Daniel J.. - : University of Rochester, 2013
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17
Semantic Role Labeling
Palmer, Martha; Gildea, Daniel; Xue, Nianwen. - : Morgan & Claypool Publishers, 2011
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18
On word alignment models for statistical machine translation
Zhao, Shaojun; Gildea, Daniel J.. - : University of Rochester, 2011
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19
Do grammars minimize dependency length?
In: Cognitive science. - Hoboken, NJ : Wiley-Blackwell 34 (2010) 2, 286-310
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OLC Linguistik
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20
Semantic role labeling
Palmer, Martha Stone; Gildea, Daniel; Xue, Nianwen. - San Rafael, Calif. : Morgan & Claypool, 2010
UB Frankfurt Linguistik
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