Abstract
In this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing entity pairs through sentence tokens and through shortest path in dependency tree, comparing approaches based on convolutional and recurrent neural networks. With convolutional network applied to shortest path in dependency tree we managed to be ranked eighth in subtask 1.1 (“clean data”), ninth in 1.2 (“noisy data”). Similar model applied to separate parts of the shortest path was mounted to ninth (extraction track) and seventh (classification track) positions in subtask 2 ranking.- Anthology ID:
- S18-1131
- 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:
- 821–825
- Language:
- URL:
- https://aclanthology.org/S18-1131
- DOI:
- 10.18653/v1/S18-1131
- Cite (ACL):
- Andrey Sysoev and Vladimir Mayorov. 2018. Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 821–825, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers (Sysoev & Mayorov, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S18-1131.pdf