Abstract
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.- Anthology ID:
- D17-1181
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1723–1729
- Language:
- URL:
- https://aclanthology.org/D17-1181
- DOI:
- 10.18653/v1/D17-1181
- Cite (ACL):
- Heike Adel and Hinrich Schütze. 2017. Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1723–1729, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification (Adel & Schütze, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-1181.pdf