LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction

Authors

  • Bonan Min Raytheon BBN Technologies
  • Manaj Srivastava Raytheon BBN Technologies
  • Haoling Qiu Raytheon BBN Technologies
  • Prasannakumar Muthukumar Raytheon BBN Technologies
  • Joshua Fasching Raytheon BBN Technologies

DOI:

https://doi.org/10.1609/aaai.v34i09.7102

Abstract

We present a system which allows a user to create event-event relation extractors on-demand with a small amount of effort. The system provides a suite of algorithms, flexible workflows, and a user interface (UI), to allow rapid customization of event-event relation extractors for new types and domains of interest. Experiments show that it enables users to create extractors for 6 types of causal and temporal relations, with less than 20 minutes of effort per type. Our system (source code, UI) is available at https://github.com/BBN-E/LearnIt. A demonstration video is available at https://vimeo.com/329950144.

AAAI-20, IAAI-20, EAAI-20 Proceedings Cover

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Published

2020-04-03

How to Cite

Min, B., Srivastava, M., Qiu, H., Muthukumar, P., & Fasching, J. (2020). LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13630-13631. https://doi.org/10.1609/aaai.v34i09.7102

Issue

Vol. 34 No. 09: Issue 9: EAAI-20 / AAAI Special Programs

Section

Demonstration Track