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IRISA System for Entity Detection and Linking at CLEF HIPE 2020

Abstract : This note describes IRISA's system for the task of named entity processing on historical newspapers in French. Following a standard entity detection and linking pipeline, our system implements three steps to solve the named entity linking task. Named Entity Recognition (NER) is first performed to identify the entity mentions in a document based on a Conditional Random Fields classifier. Candidate entities from Wikidata are then generated for each mention found, using simple search. Finally, every mention is linked to one of its candidate entities in a so-called linking step leveraging various string metrics and the semantic structure of Wikidata to improve on the linking decisions.
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Contributor : CHEIKH BRAHIM EL VAIGH Connect in order to contact the contributor
Submitted on : Tuesday, September 22, 2020 - 11:15:33 AM
Last modification on : Saturday, August 6, 2022 - 3:32:48 AM


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  • HAL Id : hal-02943717, version 2


Cheikh Brahim El Vaigh, Guillaume Le Noé-Bienvenu, Guillaume Gravier, Pascale Sébillot. IRISA System for Entity Detection and Linking at CLEF HIPE 2020. CEUR Workshop Proceedings, Sep 2020, Thessaloniki, Greece. ⟨hal-02943717v2⟩



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