Abstract
We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.- Anthology ID:
- E17-2119
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 752–758
- Language:
- URL:
- https://aclanthology.org/E17-2119
- DOI:
- Cite (ACL):
- Sanjeev Karn, Ulli Waltinger, and Hinrich Schütze. 2017. End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 752–758, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification (Karn et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/E17-2119.pdf
- Data
- FIGER