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Michael Bloodgood
Michael Bloodgood
Verified email at tcnj.edu - Homepage
Title
Cited by
Cited by
Year
A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping
M Bloodgood, K Vijay-Shanker
arXiv preprint arXiv:1409.5165, 2014
902014
Bucking the trend: Large-scale cost-focused active learning for statistical machine translation
M Bloodgood, C Callison-Burch
arXiv preprint arXiv:1410.5877, 2014
762014
Modality and negation in SIMT use of modality and negation in semantically-informed syntactic MT
K Baker, M Bloodgood, BJ Dorr, C Callison-Burch, NW Filardo, C Piatko, ...
Computational Linguistics 38 (2), 411-438, 2012
672012
A modality lexicon and its use in automatic tagging
K Baker, M Bloodgood, BJ Dorr, NW Filardo, L Levin, C Piatko
Proceedings of the Seventh International Conference on Language Resources …, 2010
61*2010
Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets
M Bloodgood, K Vijay-Shanker
arXiv preprint arXiv:1409.4835, 2014
592014
Using Mechanical Turk to build machine translation evaluation sets
M Bloodgood, C Callison-Burch
arXiv preprint arXiv:1410.5491, 2014
542014
Statistical modality tagging from rule-based annotations and crowdsourcing
V Prabhakaran, M Bloodgood, M Diab, B Dorr, L Levin, CD Piatko, ...
arXiv preprint arXiv:1503.01190, 2015
302015
Support vector machine active learning algorithms with query-by-committee versus closest-to-hyperplane selection
M Bloodgood
2018 IEEE 12th International Conference on Semantic Computing (ICSC), 148-155, 2018
272018
Analysis of stopping active learning based on stabilizing predictions
M Bloodgood, J Grothendieck
Proceedings of the Seventeenth Conference on Computational Natural Language …, 2013
262013
Translation memory retrieval methods
M Bloodgood, B Strauss
Proceedings of the 14th Conference of the European Chapter of the …, 2014
242014
Semantically informed machine translation (SIMT)
K Baker, S Bethard, M Bloodgood, R Brown, C Callison-Burch, ...
SCALE summer workshop final report, Human Language Technology Center Of …, 2009
24*2009
Semantically-informed syntactic machine translation: A tree-grafting approach
K Baker, M Bloodgood, C Callison-Burch, BJ Dorr, NW Filardo, L Levin, ...
Proceedings of the Ninth Conference of the Association for Machine …, 2010
22*2010
Stopping active learning based on predicted change of f measure for text classification
M Altschuler, M Bloodgood
2019 IEEE 13th International Conference on Semantic Computing (ICSC), 47-54, 2019
182019
Filtering tweets for social unrest
A Mishler, K Wonus, W Chambers, M Bloodgood
2017 IEEE 11th International Conference on Semantic Computing (ICSC), 17-23, 2017
182017
Impact of batch size on stopping active learning for text classification
G Beatty, E Kochis, M Bloodgood
2018 IEEE 12th International Conference on Semantic Computing (ICSC), 306-307, 2018
162018
The use of unlabeled data versus labeled data for stopping active learning for text classification
G Beatty, E Kochis, M Bloodgood
2019 IEEE 13th International Conference on Semantic Computing (ICSC), 287-294, 2019
142019
Correcting errors in digital lexicographic resources using a dictionary manipulation language
D Zajic, M Maxwell, D Doermann, P Rodrigues, M Bloodgood
Proceedings of Electronic Lexicography in the 21st Century (eLex), 297–301, 2011
122011
SIMT SCALE 2009-Modality Annotation Guidelines
K Baker, M Bloodgood, M Diab, B Dorr, E Hovy, L Levin, M McShane, ...
Technical Report. Johns Hopkins, Baltimore, 2009
10*2009
Rapid adaptation of pos tagging for domain specific uses
J Miller, M Bloodgood, M Torii, K Vijay-Shanker
Association for Computational Linguistics, 2006
62006
Tissue homogeneity requires inhibition of unequal gene silencing during development
HH Le, M Looney, B Strauss, M Bloodgood, AM Jose
Journal of Cell Biology 214 (3), 319-331, 2016
52016
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