End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Abstract:
We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder 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
Title: End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Authors: Sanjeev Karn, Ulli Waltinger, Hinrich Schütze
Pub/Conf: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017)
BibTeX:
@InProceedings{
E17-2119,
author = "Karn, Sanjeev and Waltinger, Ulli and Sch{\"u}tze, Hinrich",
title = "End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification",
booktitle = "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
year = "2017",
publisher = "Association for Computational Linguistics",
pages = "752--758",
location = "Valencia, Spain",
url = "http://aclweb.org/anthology/E17-2119"
}
