Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

Peng Xu, Denilson Barbosa


Abstract
The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.
Anthology ID:
N18-1002
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16–25
Language:
URL:
https://aclanthology.org/N18-1002
DOI:
10.18653/v1/N18-1002
Bibkey:
Cite (ACL):
Peng Xu and Denilson Barbosa. 2018. Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 16–25, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss (Xu & Barbosa, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1002.pdf
Video:
 https://aclanthology.org/N18-1002.mp4
Code
 billy-inn/NFETC +  additional community code
Data
FIGER