# Trust, but verify! Better entity linking through automatic verification

## Abstract

We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method \emph{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 \emph{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.

Item Type: Conference or Workshop Item 2017 Heinzerling, Benjamin ; Strube, Michael ; Lin, Chin-Yew Trust, but verify! Better entity linking through automatic verification German We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method \emph{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 \emph{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. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, 3--7 April 2017 Entity Linking;AIPHES_area_a1 DFG-GraduiertenkollegsDFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources 10 Jul 2017 14:02 http://aclweb.org/anthology/E17-1078 TUD-CS-2017-0179 ASCII CitationBibTeXMultiline CSVEP3 XMLDublin CoreIBW_RDAEndNoteAtomT2T_XMLReference ManagerRDF+XMLMODSHTML CitationJSONSimple Metadata TUfind oder in Google
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