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

Bonan Min, Manaj Srivastava, Haoling Qiu, Prasannakumar Muthukumar, Joshua Fasching

AAAI 2020 pp. 13630-13631

doi:10.1609/AAAI.V34I09.7102 /aaai/2020/min2020aaai-learnit/

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.

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Cite

Text

Min et al. "LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7102

Markdown

[Min et al. "LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/min2020aaai-learnit/) doi:10.1609/AAAI.V34I09.7102

BibTeX

@inproceedings{min2020aaai-learnit,
  title     = {{LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction}},
  author    = {Min, Bonan and Srivastava, Manaj and Qiu, Haoling and Muthukumar, Prasannakumar and Fasching, Joshua},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13630-13631},
  doi       = {10.1609/AAAI.V34I09.7102},
  url       = {https://mlanthology.org/aaai/2020/min2020aaai-learnit/}
}