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Modeling Syntactic Context Improves Morphological Segmentation
Yoong Keok Lee
|
Aria Haghighi
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Regina Barzilay
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Paper Details:
Month: June
Year: 2011
Location: Portland, Oregon, USA
Venue:
CoNLL |
WS |
SIG: SIGNLL
Citations
URL
Exploring the Utility of Joint Morphological and Syntactic Learning from Child-directed Speech
Stella Frank
|
Frank Keller
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Sharon Goldwater
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Adaptor Grammars for Learning Non-Concatenative Morphology
Jan A. Botha
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Phil Blunsom
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Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks
Ke M. Tran
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Arianna Bisazza
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Christof Monz
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From Segmentation to Analyses: a Probabilistic Model for Unsupervised Morphology Induction
Toms Bergmanis
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Sharon Goldwater
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Unsupervised Morphology Rivals Supervised Morphology for Arabic MT
David Stallard
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Jacob Devlin
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Michael Kayser
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Yoong Keok Lee
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Regina Barzilay
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Minimally-Supervised Morphological Segmentation using Adaptor Grammars
Kairit Sirts
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Sharon Goldwater
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An Unsupervised Method for Uncovering Morphological Chains
Karthik Narasimhan
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Regina Barzilay
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Tommi Jaakkola
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Unsupervised Learning of Morphological Forests
Jiaming Luo
|
Karthik Narasimhan
|
Regina Barzilay
|
Do POS Tags Help to Learn Better Morphological Segmentations?
Kairit Sirts
|
Mark Johnson
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Learning non-concatenative morphology
Michelle Fullwood
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Tim O’Donnell
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What Matters Most in Morphologically Segmented SMT Models?
Mohammad Salameh
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Colin Cherry
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Grzegorz Kondrak
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Automatic Glossing in a Low-Resource Setting for Language Documentation
Sarah Moeller
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Mans Hulden
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http://groups.csail.mit.edu/rbg/code/morphsyn/
Field Of Study
Linguistic Trends
Syntax
Morphology
Task
Morphological Analysis
Tagging
Word Segmentation
Approach
Bayesian Model
Unsupervised Learning
Language
Multilingual
Arabic
Hebrew
Semitic
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