Eric Horvitz


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Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
Tom Hope | Aida Amini | David Wadden | Madeleine van Zuylen | Sravanthi Parasa | Eric Horvitz | Daniel Weld | Roy Schwartz | Hannaneh Hajishirzi
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.


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Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models
Maarten Sap | Eric Horvitz | Yejin Choi | Noah A. Smith | James Pennebaker
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.

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SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Tom Hope | Jason Portenoy | Kishore Vasan | Jonathan Borchardt | Eric Horvitz | Daniel Weld | Marti Hearst | Jevin West
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over 15K users with over 42K page views and 13% returns.


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Filling the Blanks (hint: plural noun) for Mad Libs Humor
Nabil Hossain | John Krumm | Lucy Vanderwende | Eric Horvitz | Henry Kautz
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.


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Identifying Dogmatism in Social Media: Signals and Models
Ethan Fast | Eric Horvitz
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent
Zhou Yu | Dan Bohus | Eric Horvitz
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue


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Towards Situated Collaboration
Dan Bohus | Ece Kamar | Eric Horvitz
NAACL-HLT Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012)


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Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions
Dan Bohus | Eric Horvitz
Proceedings of the SIGDIAL 2011 Conference


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Models for Multiparty Engagement in Open-World Dialog
Dan Bohus | Eric Horvitz
Proceedings of the SIGDIAL 2009 Conference

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Learning to Predict Engagement with a Spoken Dialog System in Open-World Settings
Dan Bohus | Eric Horvitz
Proceedings of the SIGDIAL 2009 Conference


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Optimizing Automated Call Routing by Integrating Spoken Dialog Models with Queuing Models
Tim Paek | Eric Horvitz
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004


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Using Machine Learning Techniques to Interpret WH-questions
Ingrid Zukerman | Eric Horvitz
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics