Abstract
Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering <predicate sense, object head> pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations.- Anthology ID:
- 2021.emnlp-main.441
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5427–5440
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.441
- DOI:
- 10.18653/v1/2021.emnlp-main.441
- Cite (ACL):
- Jiaming Shen, Yunyi Zhang, Heng Ji, and Jiawei Han. 2021. Corpus-based Open-Domain Event Type Induction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5427–5440, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Corpus-based Open-Domain Event Type Induction (Shen et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.emnlp-main.441.pdf
- Code
- mickeystroller/etypeclus
- Data
- Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison