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14 results found

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.

Publication: Journal Article

Savova, Guergana K., Ioana Danciu, Folami Alamudun, Timothy Miller, Chen Lin, Danielle S. Bitterman, Georgia Tourassi, and Jeremy L. Warner. 2019. “Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.”. Cancer Research 79 (21): 5463–5470.

Cross-document coreference: An approach to capturing coreference without context

Publication: Conference Paper

Wright-Bettner, Kristin, Martha Palmer, Guergana Savova, Piet Groen, and Timothy Miller. 2019. “Cross-Document Coreference: An Approach to Capturing Coreference Without Context”. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), 1–10. Hong Kong: Association for Computational Linguistics.

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...

Simplified Neural Unsupervised Domain Adaptation

Publication: Conference Paper

Miller, Timothy. 2019. “Simplified Neural Unsupervised Domain Adaptation”. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 414–419. Minneapolis, Minnesota: Association for Computational Linguistics.

A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction

Publication: Conference Paper

Lin, Chen, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2019. “A BERT-Based Universal Model for Both Within- and Cross-Sentence Clinical Temporal Relation Extraction”. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, 65–71. Minneapolis, Minnesota, USA: Association for Computational Linguistics.

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.