End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

Sanjeev Karn, Ulli Waltinger, Hinrich Schütze

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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
752–758
Language:
URL:
https://aclanthology.org/E17-2119
DOI:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/starsem-semeval-split/E17-2119.pdf
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