Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
Klim Zaporojets, Johannes Deleu, Yiwei Jiang, Thomas Demeester, Chris Develder
Abstract
We consider the task of document-level entity linking (EL), where it is important to make consistent decisions for entity mentions over the full document jointly. We aim to leverage explicit “connections” among mentions within the document itself: we propose to join EL and coreference resolution (coref) in a single structured prediction task over directed trees and use a globally normalized model to solve it. This contrasts with related works where two separate models are trained for each of the tasks and additional logic is required to merge the outputs. Experimental results on two datasets show a boost of up to +5% F1-score on both coref and EL tasks, compared to their standalone counterparts. For a subset of hard cases, with individual mentions lacking the correct EL in their candidate entity list, we obtain a +50% increase in accuracy.
BibTeX
@inproceedings{zaporojets-etal-2022-towards,
title = "Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution",
author = "Zaporojets, Klim and
Deleu, Johannes and
Jiang, Yiwei and
Demeester, Thomas and
Develder, Chris",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.88/",
doi = "10.18653/v1/2022.acl-short.88",
pages = "778--784"
}