Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation

Samuel Kriman, Heng Ji


Abstract
Constructing knowledge graphs from unstructured text is an important task that is relevant to many domains. Most previous work focuses on extracting information from sentences or paragraphs, due to the difficulty of analyzing longer contexts. In this paper we propose a new jointly trained model that can be used for various information extraction tasks at the document level. The tasks performed by this system are entity and event identification, typing, and coreference resolution. In order to improve entity and event typing, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and events across the entire document. By extending our system to document-level, we can improve our results by incorporating cross-sentence dependencies and additional contextual information that might not be available at the sentence level, which allows for more globally optimized predictions. We evaluate our system on documents from the ACE05-E+ dataset and find significant improvement over the sentence-level SOTA on entity and event trigger identification and classification.
Anthology ID:
2021.acl-srw.18
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
August
Year:
2021
Address:
Online
Editors:
Jad Kabbara, Haitao Lin, Amandalynne Paullada, Jannis Vamvas
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
174–179
Language:
URL:
https://aclanthology.org/2021.acl-srw.18
DOI:
10.18653/v1/2021.acl-srw.18
Bibkey:
Cite (ACL):
Samuel Kriman and Heng Ji. 2021. Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 174–179, Online. Association for Computational Linguistics.
Cite (Informal):
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation (Kriman & Ji, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-srw.18.pdf
Video:
 https://aclanthology.org/2021.acl-srw.18.mp4