Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds

Sheng Zhang, Kevin Duh, Benjamin Van Durme


Abstract
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context – both document and sentence level information – than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.
Anthology ID:
S18-2022
Volume:
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–179
Language:
URL:
https://www.aclweb.org/anthology/S18-2022
DOI:
10.18653/v1/S18-2022
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-2022.pdf