@inproceedings{zhang-etal-2018-fine,
title = "Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds",
author = "Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2022/",
doi = "10.18653/v1/S18-2022",
pages = "173--179",
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."
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%0 Conference Proceedings
%T Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
%A Zhang, Sheng
%A Duh, Kevin
%A Van Durme, Benjamin
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F zhang-etal-2018-fine
%X 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.
%R 10.18653/v1/S18-2022
%U https://aclanthology.org/S18-2022/
%U https://doi.org/10.18653/v1/S18-2022
%P 173-179
Markdown (Informal)
[Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds](https://aclanthology.org/S18-2022/) (Zhang et al., *SEM 2018)
ACL