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Hits 21 – 40 of 41
21
1 A Morphological Analyzer for Verbal Aspect in American Sign Language
Aaron Shield
;
Jason Baldridge
In: http://comp.ling.utexas.edu/jbaldrid/papers/shield_baldridge_tlsx.pdf
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22
1 A Morphological Analyzer for Verbal Aspect in American Sign Language
Aaron Shield
;
Jason Baldridge
In: http://comp.ling.utexas.edu/jbaldrid/papers/shield_baldridge_tlsx_prefinal.pdf
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23
9 A Morphological Analyzer for Verbal As- pect in American Sign Language
Nicholas Gaylord
;
Stephen Hilderbr
;
Heeyoung Lyu
...
In: http://www.stanford.edu/group/cslipublications/cslipublications/TLS/TLS10-2006/TLS10_Shield_Baldridge.pdf
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24
2007 – 2008 Research Assistant, EARL – Efficient Annotation of Resources using Learning;
Alexis M. Palmer
;
Supervisors Jonas Kuhn
;
Carlota Smith
...
In: http://www.coli.uni-saarland.de/~apalmer/docs/palmer_cv_may2014.pdf
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25
Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph
Michael Speriosu
;
Sid Upadhyay
;
Nikita Sudan
...
In: http://www.aclweb.org/anthology-new/W/W11/W11-2207.pdf
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26
Minimized models and grammar-informed initialization for supertagging
Sujith Ravi
;
Jason Baldridge
;
Kevin Knight
In: http://www.isi.edu/natural-language/mt/acl10.min.pdf
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27
Providing Robustness for a CCG System
Julia Hockenmaier
;
Gann Bierner
;
Jason Baldridge
In: http://ling.osu.edu/~dm/events/esslli00/submissions/sub10.ps.gz
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28
EDUCATION
Alexis Palmer
;
Supervisors Jason Baldridge
;
Katrin Erk
In: http://www.coli.uni-saarland.de/%7Eapalmer/docs/palmer-cv.pdf
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29
Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models
Elias Ponvert
;
Jason Baldridge
;
Katrin Erk
In: http://aclweb.org/anthology-new/P/P11/P11-1108.pdf
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30
Annotation for and Robust Parsing of Discourse Structure on Unrestricted Texts
Jason Baldridge
;
Nicholas Asher
;
Julie Hunter
In: http://comp.ling.utexas.edu/jbaldrid/papers/baldridge_etal_sprach07.pdf
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31
Supervised Text-based Geolocation Using Language Models on an Adaptive Grid
Stephen Roller
;
Michael Speriosu
;
Sarat Rallapalli
...
In: http://www.aclweb.org/anthology-new/D/D12/D12-1137.pdf
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32
CSLI Studies in Computational Linguistics ONLINE
Jason Baldridge
;
Sudipta Chatterjee
;
Alexis Palmer
...
In: http://comp.ling.utexas.edu/apalmer/geaf07baldridgeetal.pdf
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33
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
Dan Garrette
;
Jason Mielens
;
Jason Baldridge
In: http://www.cs.utexas.edu/users/ml/papers/garrette.acl13.pdf
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34
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
Dan Garrette
;
Jason Mielens
;
Jason Baldridge
In: http://www.cs.utexas.edu/~dhg/papers/garrette_mielens_baldridge_acl2013.pdf
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35
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
Dan Garrette
;
Jason Mielens
;
Jason Baldridge
In: http://aclweb.org/anthology/P/P13/P13-1057.pdf
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36
Weakly-Supervised Bayesian Learning of a CCG Supertagger
Dan Garrette
;
Chris Dyer
;
Jason Baldridge
...
In: http://www.cs.utexas.edu/~dhg/papers/garrette_dyer_baldridge_smith_conll2014.pdf
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37
A Tool for Semi-Automatic and Interactive Annotation of Dialogue
Utterances With Information
;
David Schlangen
;
Alex Lascarides
...
In: http://www.cogsci.ed.ac.uk/~das/papers/schlascbald.pdf
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38
Weakly-Supervised Bayesian Learning of a CCG Supertagger
Dan Garrette
;
Chris Dyer
;
Jason Baldridge
...
In: http://www.cs.cmu.edu/~nasmith/papers/garrette+dyer+baldridge+smith.conll14.pdf
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39
Learning a Part-of-Speech Tagger from Two Hours of Annotation
Dan Garrette
;
Jason Baldridge
In: http://aclweb.org/anthology-new/N/N13/N13-1014.pdf
Abstract:
Most work on weakly-supervised learning for part-of-speech taggers has been based on unrealistic assumptions about the amount and quality of training data. For this paper, we attempt to create true low-resource scenarios by allowing a linguist just two hours to annotate data and evaluating on the languages Kinyarwanda and Malagasy. Given these severely limited amounts of either type supervision (tag dictionaries) or token supervision (labeled sentences), we are able to dramatically improve the learning of a hidden Markov model through our method of automatically generalizing the annotations, reducing noise, and inducing word-tag frequency information. 1
URL:
http://aclweb.org/anthology-new/N/N13/N13-1014.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.353.9634
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40
courses, tools
Jason Baldridge
;
Katrin Erk
In: http://aclweb.org/anthology-new/W/W08/W08-0201.pdf
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