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Trust, but verify! Better entity linking through automatic verification

Heinzerling, Benjamin and Strube, Michael and Lin, Chin-Yew (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, Valencia, Spain, 3--7 April 2017, [Online-Edition: http://aclweb.org/anthology/E17-1078],
[Conference or Workshop Item]

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
Erschienen: 2017
Creators: Heinzerling, Benjamin and Strube, Michael and Lin, Chin-Yew
Title: Trust, but verify! Better entity linking through automatic verification
Language: German
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.

Title of Book: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, 3--7 April 2017
Uncontrolled Keywords: Entity Linking;AIPHES_area_a1
Divisions: DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 10 Jul 2017 14:02
Official URL: http://aclweb.org/anthology/E17-1078
Identification Number: TUD-CS-2017-0179
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