Modality and Negation in Event Extraction

Sander Bijl de Vroe, Liane Guillou, Miloš Stanojević, Nick McKenna, Mark Steedman


Abstract
Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.
Anthology ID:
2021.case-1.6
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–42
Language:
URL:
https://aclanthology.org/2021.case-1.6
DOI:
10.18653/v1/2021.case-1.6
Bibkey:
Cite (ACL):
Sander Bijl de Vroe, Liane Guillou, Miloš Stanojević, Nick McKenna, and Mark Steedman. 2021. Modality and Negation in Event Extraction. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 31–42, Online. Association for Computational Linguistics.
Cite (Informal):
Modality and Negation in Event Extraction (Bijl de Vroe et al., CASE 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.6.pdf
Code
 lianeg/montee
Data
FIGER