ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction

Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang


Abstract
Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 Subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.
Anthology ID:
S18-1112
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
689–696
Language:
URL:
https://aclanthology.org/S18-1112
DOI:
10.18653/v1/S18-1112
Bibkey:
Cite (ACL):
Jonathan Rotsztejn, Nora Hollenstein, and Ce Zhang. 2018. ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 689–696, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction (Rotsztejn et al., SemEval 2018)
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PDF:
https://aclanthology.org/S18-1112.pdf