Peter A Jansen
- Assistant Professor, School of Information
I am a broadly interdisciplinary artificial intelligence researcher specializing in natural language processing and methods inspired by cognition and the brain. I apply these to application areas in science and health care.
A central focus of my science research is on how we can teach computers question answering in the form of passing standardized science exams, as written. In particular, I focus on methods of automated inference that generate explanations for why the answer is correct, largely using graph-based methods.
In terms of health care, I study how we can use natural language processing and inference to improve electronic health records and improve nurse communication, as well as detect potentially dangerous clinical events before they happen.
I uniquely have two distinct educational backgrounds, one in natural language processing, cognition, and computer science, the other in physics, electrical engineering, and sensing. I maintain active outreach in grounding science education through sensing, largely in the form of open source hardware like the tricorder project, and projects like the open source computed tomography scanner. This work has been widely featured in over 50 international news media articles, including Reuters, Forbes, WIRED, MSNBC, and the Washington Post, as well as an invited talk at TEDxBrussels 2012. In 2015, my open source science tricorder was honoured by being placed on permanent exhibit at the German Museum of Technology in Berlin.
- Ph.D. Psychology and Neuroscience
- McMaster University, Hamilton, Ontario, Canada
- A self-organizing computational neural network architecture with applications to sensorimotor grounded linguistic grammar acquisition.
- BIS Independent Studies (Computer Science and Physics), Cognitive Science Option
- University of Waterloo, Waterloo, Ontario, Canada
- Developmental knowledge represenation: a proposal for the representational substrate
- University of Arizona, Tucson, Arizona (2016 - Ongoing)
- University of Arizona, Tucson, Arizona (2015 - 2016)
- University of Arizona, Tucson, Arizona (2013 - 2015)
- University of Arizona, Tucson, Arizona (2010 - 2012)
Artificial Intelligence, Natural Language Processing, Rapid Prototyping
Artificial Intelligence, Natural Language Processing, Automated Inference, Semantic Knowledge Representation, Question Answering, Explainable Inference, Cognitive Science
Directed ResearchINFO 692 (Summer I 2020)
Graduate SeminarINFO 696E (Spring 2020)
Intro to Creative CodingISTA 303 (Fall 2019)
Independent StudyINFO 699 (Spring 2019)
Natural Language ProcessingISTA 355 (Spring 2019)
Intro to Creative CodingISTA 303 (Fall 2018)
Directed ResearchINFO 692 (Fall 2017)
Intro to Creative CodingISTA 303 (Fall 2017)
Intro to Creative CodingISTA 303 (Spring 2017)
Intro: Human Computer InteractINFO 516 (Fall 2016)
Intro: Human Computer InteractISTA 416 (Fall 2016)
Language+ComputersLING 388 (Spring 2016)
- Jansen, P. A., Sharp, R., Surdeanu, M., & Clark, P. (2017). Framing Question Answering as Building and Ranking Answer Justifications. Computational Linguistics, 40.
- Fried, D., Jansen, P., Hahn-Powell, G., Surdeanu, M., & Clark, P. (2015). Higher-order Lexical Semantic Models for Non-factoid Answer Reranking. Transactions of the Association for Computational Linguistics, 3, 197--210.
- Jansen, P., & Ustalov, D. (2019). TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13).
- Khot, T., Clark, P., Guerquin, M., Jansen, P., & Sabharwal, A. (2020, Fall). QASC: A Dataset for Question Answering via Sentence Composition. In AAAI.
- Thiem, S., & Jansen, P. (2019). Extracting Common Inference Patterns from Semi-Structured Explanations. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing.
- Jansen, P. (2018). Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?. In Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12).
- Jansen, P. A., Jansen, P. A., Wainwright, E., Wainwright, E., Marmorstein, S., Marmorstein, S., Morrison, C. T., & Morrison, C. T. (2018, May). WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference. In Language Resources and Evaluation Conference (LREC).
- Kwan, H., Trivedi, H., Jansen, P. A., Surdeanu, M., & Balasubramanian, N. (2018, March). Controlling Information Aggregation for Complex Question Answering. In European Conference on Information Retrieval (ECIR).
- Jansen, P. A. (2017, December). A Study of Automatically Acquiring Explanatory Inference Patterns from Corpora of Explanations: Lessons from Elementary Science Exams. In The 2017 Workshop on Automatic Knowledge Base Construction (AKBC'2017).
- Sharp, R., Surdeanu, M., Jansen, P. A., Valenzuela-Escarcega, M., Clark, P., & Hammond, M. (2017, August). Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification. In SIGNLL Conference on Computational Natural Language Learning (CoNLL).
- Jansen, P., Balasubramanian, N., Surdeanu, M., & Clark, P. (2016, December). What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams. In Proceedings of the 26th International Conference on Computational Linguistics (COLING).
- Sharp, R., Surdeanu, M., Jansen, P., Clark, P., & Hammond, M. (2016, Fall). Creating Causal Embeddings for Question Answering with Minimal Supervision. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).
- Sharp, R., Jansen, P., Surdeanu, M., & Clark, P. (2015, Spring). Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).
- Jansen, P., Surdeanu, M., & Clark, P. (2014, Summer). Discourse Complements Lexical Semantics for Non-factoid Answer Reranking. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL).
- Forbes, A., Surdeanu, M., Jansen, P., & Carrington, J. (2013). Transmitting Narrative: An Interactive Shift-Summarization Tool for Improving Nurse Communication. In Proceedings of the 3rd IEEE Workshop on Interactive Visual Text Analytics.