| 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 | 116 | 2014 |
| Bucking the trend: Large-scale cost-focused active learning for statistical machine translation M Bloodgood, C Callison-Burch arXiv preprint arXiv:1410.5877, 2014 | 89 | 2014 |
| 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 | 74 | 2012 |
| 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 | 71* | 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 | 66 | 2014 |
| Using Mechanical Turk to build machine translation evaluation sets M Bloodgood, C Callison-Burch arXiv preprint arXiv:1410.5491, 2014 | 56 | 2014 |
| 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 | 33 | 2015 |
| Analysis of stopping active learning based on stabilizing predictions M Bloodgood, J Grothendieck Proceedings of the Seventeenth Conference on Computational Natural Language …, 2013 | 32 | 2013 |
| 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 | 31 | 2018 |
| Translation memory retrieval methods M Bloodgood, B Strauss Proceedings of the 14th Conference of the European Chapter of the …, 2014 | 28 | 2014 |
| 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 | 26 | 2019 |
| 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 | 24 | 2019 |
| 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 | 23* | 2009 |
| 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 | 21 | 2017 |
| 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 | 20* | 2010 |
| 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 | 19 | 2018 |
| 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 | 12 | 2011 |
| Data cleaning for xml electronic dictionaries via statistical anomaly detection M Bloodgood, B Strauss 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 79-86, 2016 | 10 | 2016 |
| Using global constraints and reranking to improve cognates detection M Bloodgood, B Strauss arXiv preprint arXiv:1704.07050, 2017 | 8 | 2017 |
| 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 | 8* | 2009 |
