Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. A large annotated corpus for learning natural language inference (corpus page). Proceedings of EMNLP. 2015. Best New Data Set or Resource Award.
Samuel R. Bowman, Christopher D. Manning, and Christopher Potts. Tree-structured composition in neural networks without tree-structured architectures. arXiv manuscript 1506.04834. 2015.
Samuel R. Bowman, Christopher Potts, and Christopher D. Manning. Recursive Neural Networks Can Learn Logical Semantics (MATLAB source code and data, poster). Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. 2015.
Samuel R. Bowman, Christopher Potts, and Christopher D. Manning. Learning Distributed Word Representations for Natural Logic Reasoning. Proceedings of the AAAI Spring Symposium on Knowledge Representation and Reasoning. 2015.
Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel R. Bowman, Miriam Connor, John Bauer, and Christopher D. Manning. A Gold Standard Dependency Corpus for English. Proceedings of LREC 9. 2014.
Samuel R. Bowman. Can recursive neural tensor networks learn logical reasoning? (MATLAB source code and data). arXiv manuscript 1312.6192. Presented at ICLR '14 and as an invited talk at the 3rd CSLI Workshop on Logic, Rationality and Intelligent Interaction. 2014.
Samuel R. Bowman. Transparent vowels in ABC: open issues. UC Berkeley Phonology Lab Annual Report: Conference on Agreement by Correspondence (ABC↔Conference; handout). Invited talk. 2014.
Samuel R. Bowman and Benjamin Lokshin. Idiosyncratic transparent vowels in Kazakh. Proceedings of the 2013 Meeting on Phonology, presentations at Berkeley Phorum and WAFL 9. 2013. A typo in item (39) in the published version is corrected here.
Marie-Catherine de Marneffe, Miriam Connor, Natalia Silveira, Samuel R. Bowman, Timothy Dozat and Christopher D. Manning. More constructions, more genres: Extending Stanford Dependencies. Proceedings of DepLing. 2013.
Samuel R. Bowman. Two arguments for vowel harmony by trigger competition. Proceedings of CLS 49, presentations at Edinburgh P-Workshop and 21mfm. 2013.
Robert Podesva, Annette D'Onofrio, Eric Acton, Sam Bowman, Jeremy Calder, Hsin-Chang Chen, Benjamin Lokshin, and Janneke Van Hofwegen. Linguistic and social effects on perceptions of voice onset time in Korean stops (abstract). Presentation at ASA 164. 2012.
Samuel R. Bowman. Vowel varmony, opacity, and finite-state OT. Technical report TR-2011-03, Department of Computer Science, The University of Chicago.
Geoffrey Zweig, Les Atlas, Kris Demuynck, Fei Sha, Patrick Nguyen, Dirk van Compernolle, Damianos Karakos, Pascal Clark, Meihong Wang, Gregory Sell, Samuel Thomas, Samuel R. Bowman and Justine Kao. Speech recognition with segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop. Proceedings of ICASSP 36. 2011.
Samuel R. Bowman and Karen Livescu. Modeling pronunciation variation with context-dependent articulatory feature decision trees. Proceedings of Interspeech. 2010.
Samuel R. Bowman. Neural networks for natural language understanding (slides, 2014 slides). Guest lecture for Chris Potts and Bill MacCartney's computational natural language understanding class. 2015.
Samuel R. Bowman. vector-entailment: A work-in-progress MATLAB toolkit for recursive neural networks for textual entailment. 2015.
Samuel R. Bowman. Squib draft: Sour grapes harmony and the Agree constraint. 2012.
Samuel R. Bowman. Course paper for Metrics with Paul Kiparsky: Automatic Parsing for Shakespearean Meters. 2011.
Jason Riggle, Max Bane, and Samuel R. Bowman. PyPhon. (A software package for finite-state Optimality Theory.)
Samuel R. Bowman. Tutorial: Building OT Grammars in PyPhon (slides). Presentation at the Stanford P-Interest Workshop. 2012.