SinNer@Clef-Hipe2020 : Sinful adaptation of SotA models for Named Entity Recognition in French and German

Pedro Javier Ortiz Suárez (1, 2) , Yoann Dupont (3, 4) , Gaël Lejeune (3, 4) , Tian Tian (3, 4)

1 ALMAnaCH - Automatic Language Modelling and ANAlysis & Computational Humanities
2 SU - Sorbonne Université
3 STIH - Sens, Texte, Informatique, Histoire
4 STIH-LC - Équipe Linguistique computationnelle

In this article we present the approaches developed by the Sorbonne-INRIA for NER (SinNer) team for the CLEF-HIPE 2020 challenge on Named Entity Processing on old newspapers. The challenge proposed various tasks for three languages, among them we focused on Named Entity Recognition in French and German texts. The best system we proposed ranked third for these two languages, it uses FastText em-beddings and Elmo language models (FrELMo and German ELMo). We show that combining several word representations enhances the quality of the results for all NE types and that the segmentation in sentences has an important impact on the results.

Mots clés: Named Entity Recognition; Historical Texts; German; French; ELMo; CRFs; Sentence Segmentation

Domaines: Informatique et langage [cs.CL]

Pedro Javier Ortiz Suárez, Yoann Dupont, Gaël Lejeune, Tian Tian. SinNer@Clef-Hipe2020 : Sinful adaptation of SotA models for Named Entity Recognition in French and German. CLEF 2020 Working Notes. Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum, Sep 2020, Thessaloniki / Virtual, Greece. ⟨hal-02984746⟩