Abstract
Most previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive. In this paper, we work on a new task of semi-supervised event type induction, aiming to automatically discover a set of unseen types from a given corpus by leveraging annotations available for a few seen types. We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations. A variational autoencoder is further introduced to enforce the reconstruction of each event mention conditioned on its latent type distribution. Experiments show that our approach can not only achieve state-of-the-art performance on supervised event detection but also discover high-quality new event types.- Anthology ID:
- 2020.emnlp-main.53
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 718–724
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.53
- DOI:
- 10.18653/v1/2020.emnlp-main.53
- Cite (ACL):
- Lifu Huang and Heng Ji. 2020. Semi-supervised New Event Type Induction and Event Detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 718–724, Online. Association for Computational Linguistics.
- Cite (Informal):
- Semi-supervised New Event Type Induction and Event Detection (Huang & Ji, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.emnlp-main.53.pdf