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8 results found

Unsupervised Grammar Induction with Depth-bounded PCFG

Publication: Journal Article

Jin, Lifeng, Finale Doshi-Velez, Timothy Miller, William Schuler, and Lane Schwartz. 2018. “Unsupervised Grammar Induction With Depth-Bounded PCFG”. Transactions of the Association for Computational Linguistics 6: 211–224.

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NLP Postdoctoral Research Fellow at Harvard Medical School Machine Learning for Medical Language Laboratory, PI Timothy Miller The Machine Learning for Medical Language Lab at Boston Children’s Hospital is seeking post-doctoral research fellows to contribute to cutting edge research in the field of...

Learning Patient Representations from Text

Publication: Conference Paper

Dligach, Dmitriy, and Timothy Miller. 2018. “Learning Patient Representations from Text”. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, 119–123. Association for Computational Linguistics.

Spotting Spurious Data with Neural Networks

Publication: Conference Paper

Amiri, Hadi, Timothy Miller, and Guergana Savova. 2018. “Spotting Spurious Data With Neural Networks”. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2006–2016. Association for Computational Linguistics.

Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction

Publication: Conference Paper

Lin, Chen, Timothy Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, and Guergana Savova. 2018. “Self-Training Improves Recurrent Neural Networks Performance for Temporal Relation Extraction”. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, 165–176. Association for Computational Linguistics.

Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction

Publication: Conference Paper

Jin, Lifeng, Finale Doshi-Velez, Timothy Miller, William Schuler, and Lane Schwartz. 2018. “Depth-Bounding Is Effective: Improvements and Evaluation of Unsupervised PCFG Induction”. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2721–2731. Association for Computational Linguistics.

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records

Publication: Journal Article

Savova, Guergana, Eugene Tseytlin, Sean Finan, Melissa Castine, Timothy Miller, Olga Medvedeva, David Harris, et al. 2017. “DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records”. Cancer Res 77 (21): e115-e118.

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