Abstract
Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.- Anthology ID:
- P19-1432
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4393–4399
- Language:
- URL:
- https://aclanthology.org/P19-1432
- DOI:
- 10.18653/v1/P19-1432
- Cite (ACL):
- Amir Pouran Ben Veyseh, Thien Huu Nguyen, and Dejing Dou. 2019. Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4393–4399, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures (Pouran Ben Veyseh et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P19-1432.pdf