Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction.M.Mitchell In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2015). In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015Ī. In Proceedings of the 33rd International Conference on Machine Learning, 2016 In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2016Į. Joint Extraction of Events and Entities within a Document Context,Ĭonference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016.Ī. Proceedings of the Association for Computational Linguistics (ACL), 2017. Leveraging Knowledge Bases in LSTMs for Improving Machine Reading International Joint Conference on Artificial Intelligence 2017. Parsing Natural Language Conversations with Contextual Cues ,.Joint Concept Learning and Semantic Parsing from Natural Language Explanations,.Neural Information Processing Systems (NIPS), December 2017.Ī Joint Sequential and Relational Modelfor Frame-Semantic Parsing ,Ĭonference on Empirical Methods on Natural Language Processing (EMNLP), 2017. Mitchell andĮstimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach ,Įmmanouil Platanios, H. Track how Technology is Transforming Work, Tom M.Mitchell, and Daniel Rock American Economic Association Papers and Proceedings, What Can Machines Learn and What Does It Mean forĮrik Brynjolfsson, Tom M.Mitchell, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018. Contextual Parameter Generation for Universal Neural Machine Translation,Į.The Lexical Semantics of Adjective-Noun Phrases in the Human Brain,Ī.Mitchell, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. Relating Simple Sentence Representations in Deep Neural Networks and the Brain, S.Myers, Proceedings of the 32nd Annual ACM Symposium on User PUMICE: A Multi-Modal Agent that Learns Concepts andĬonditionals from Natural Language and Demonstrations, T Li, M.Poczos, Proceedings of the 34th AAAI Conference on Artificial Intelligence Generation for Knowledge Graph Link Prediction, G. The 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations Language Instructions and Demonstrations, T. Interactive Task Learning from GUI-Grounded Natural.
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Myers, Proceedings of the ACM Symposium on User Interface Software and Technology (UISTĢ020),DOI:, October 2020. , October 2020.īreakdowns in Task-Oriented Dialogs, T. When is Deep Learning the Best Approach to Knowledge Tracing?, Gervet, T., Koedinger, K., Schneider, J., & Mitchell, T., Journal of Educational Data Mining,12(3), 31-54.In Proceedings of the Conference on Artificial Intelligence (AAAI), 2021. We Don't Speak the Same Language: Interpreting Polarization through.Myers, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021)DOI:10.1145/3411764.3445049. Screen2Vec: Semantic Embedding of GUI Screens and GUI Components, Toby Jia-Jun Li, Lindsay Popowski, Tom M.New Data, New Approaches, Improved Accuracy, Robin Schmucker, Jingbo Wang, Shijia Hu, and Tom Assessing the Knowledge State of Online Students.Methods in Natural Language Processing (EMNLP 2021). Mitchell, Proceedings of the 2021 Conference on Empirical Conversational Multi-Hop Reasoning with NeuralĬommonsense Knowledge and Symbolic Logic Rules, Forough Arabshahi, Jenifer Lee, Antoineīosselut, Yejin Choi, and Tom M.Michalski, Carbonell, and Mitchell, eds., Morgan-Kaufman, 1986.Ĭarbonell, and Mitchell, eds., Tioga Press, 1983. Machine Learning: An Artificial Intelligence.Selected Publications by Tom Mitchell Selected Publications by
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