TY - GEN
T1 - Joint detection and coreference resolution of entities and events with document-level context aggregation
AU - Kriman, Samuel
AU - Ji, Heng
N1 - Funding Information:
This research is based upon work supported in part by U.S. DARPA KAIROS Program No. FA8750-19-2-1004, U.S. DARPA AIDA Program No. FA8750-18-2-0014, and Air Force No. FA8650-17-C-7715. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - 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 in this paper are entity and event identification, typing, and coreference resolution. In order to improve entity and event extraction, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and event triggers 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 state-of-the-art on entity extraction and event detection.
AB - 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 in this paper are entity and event identification, typing, and coreference resolution. In order to improve entity and event extraction, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and event triggers 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 state-of-the-art on entity extraction and event detection.
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M3 - Conference contribution
AN - SCOPUS:85118916243
T3 - ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Student Research Workshop
SP - 174
EP - 179
BT - ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Student Research Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 2021 Student Research Workshop, SRW 2021 at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
Y2 - 5 August 2021 through 6 August 2021
ER -