Abstract
Supervised event extraction models require a substantial amount of training data to perform well. However, event annotation requires a lot of human effort and costs much time, which limits the application of existing supervised approaches to new event types. In order to reduce manual labor and shorten the time to build an event extraction system for an arbitrary event ontology, we present a new framework to train such systems much more efficiently without large annotations. Our event trigger labeling model uses a weak supervision approach, which only requires a set of keywords, a small number of examples and an unlabeled corpus, on which our approach automatically collects weakly supervised annotations. Our argument role labeling component performs zero-shot learning, which only requires the names of the argument roles of new event types. The source codes of our event trigger detection1 and event argument extraction2 models are publicly available for research purposes. We also release a dockerized system connecting the two models into an unified event extraction pipeline.- Anthology ID:
- 2022.deeplo-1.11
- Volume:
- Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
- Month:
- July
- Year:
- 2022
- Address:
- Hybrid
- Editors:
- Colin Cherry, Angela Fan, George Foster, Gholamreza (Reza) Haffari, Shahram Khadivi, Nanyun (Violet) Peng, Xiang Ren, Ehsan Shareghi, Swabha Swayamdipta
- Venue:
- DeepLo
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 102–109
- Language:
- URL:
- https://aclanthology.org/2022.deeplo-1.11
- DOI:
- 10.18653/v1/2022.deeplo-1.11
- Cite (ACL):
- Pengfei Yu, Zixuan Zhang, Clare Voss, Jonathan May, and Heng Ji. 2022. Building an Event Extractor with Only a Few Examples. In Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing, pages 102–109, Hybrid. Association for Computational Linguistics.
- Cite (Informal):
- Building an Event Extractor with Only a Few Examples (Yu et al., DeepLo 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.deeplo-1.11.pdf