Abstract
This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4% for the relation extraction task and 18.5% for the relation classification task with a basic configuration of the one step CNN.- Anthology ID:
- S18-1126
- 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:
- 793–797
- Language:
- URL:
- https://aclanthology.org/S18-1126
- DOI:
- 10.18653/v1/S18-1126
- Cite (ACL):
- Víctor Suárez-Paniagua, Isabel Segura-Bedmar, and Akiko Aizawa. 2018. UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 793–797, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network (Suárez-Paniagua et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/S18-1126.pdf