@article{Min_Srivastava_Qiu_Muthukumar_Fasching_2020, title={LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7102}, DOI={10.1609/aaai.v34i09.7102}, abstractNote={<p>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.</p>}, number={09}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Min, Bonan and Srivastava, Manaj and Qiu, Haoling and Muthukumar, Prasannakumar and Fasching, Joshua}, year={2020}, month={Apr.}, pages={13630-13631} }