Abstract
SemEval 2018 Task 7 focuses on relation extraction and classification in scientific literature. In this work, we present our tree-based LSTM network for this shared task. Our approach placed 9th (of 28) for subtask 1.1 (relation classification), and 5th (of 20) for subtask 1.2 (relation classification with noisy entities). We also provide an ablation study of features included as input to the network.- Anthology ID:
- S18-1133
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 831–835
- Language:
- URL:
- https://aclanthology.org/S18-1133
- DOI:
- 10.18653/v1/S18-1133
- Cite (ACL):
- Sean MacAvaney, Luca Soldaini, Arman Cohan, and Nazli Goharian. 2018. GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 831–835, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification (MacAvaney et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/S18-1133.pdf
- Code
- Georgetown-IR-Lab/semeval2018-task7