Abstract
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.- Anthology ID:
- S18-1128
- 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:
- 805–810
- Language:
- URL:
- https://aclanthology.org/S18-1128
- DOI:
- 10.18653/v1/S18-1128
- Cite (ACL):
- Farhad Nooralahzadeh, Lilja Øvrelid, and Jan Tore Lønning. 2018. SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 805–810, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers (Nooralahzadeh et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1128.pdf