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        <title>Trust, but Verify! Better Entity Linking through Automatic Verification</title>
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        <namePart type="given">Benjamin</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Trust, but Verify! Better Entity Linking through Automatic Verification
%A Heinzerling, Benjamin
%A Strube, Michael
%A Lin, Chin-Yew
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F heinzerling-etal-2017-trust
%X 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.
%U https://aclanthology.org/E17-1078
%P 828-838

Markdown (Informal)

[Trust, but Verify! Better Entity Linking through Automatic Verification](https://aclanthology.org/E17-1078) (Heinzerling et al., EACL 2017)

• Trust, but Verify! Better Entity Linking through Automatic Verification (Heinzerling et al., EACL 2017)

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.