Trust, but Verify! Better Entity Linking through Automatic Verification

Benjamin Heinzerling, Michael Strube, Chin-Yew Lin


Abstract
We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. This profile is then used to automatically verify each linked mention individually, i.e., to predict whether it has been linked correctly or not. Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7 F1 on AIDA/CoNLL’03 and up to 2.4 F1 on the English TAC KBP 2015 TEDL dataset.
Anthology ID:
E17-1078
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
828–838
Language:
URL:
https://aclanthology.org/E17-1078
DOI:
Bibkey:
Cite (ACL):
Benjamin Heinzerling, Michael Strube, and Chin-Yew Lin. 2017. Trust, but Verify! Better Entity Linking through Automatic Verification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 828–838, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Trust, but Verify! Better Entity Linking through Automatic Verification (Heinzerling et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1078.pdf