@inproceedings{moreno-etal-2019-tlr,
title = "{TLR} at {BSNLP}2019: A Multilingual Named Entity Recognition System",
author = "Moreno, Jose G. and
Linhares Pontes, Elvys and
Coustaty, Mickael and
Doucet, Antoine",
editor = "Erjavec, Toma{\v{z}} and
Marci{\'n}czuk, Micha{\l} and
Nakov, Preslav and
Piskorski, Jakub and
Pivovarova, Lidia and
{\v{S}}najder, Jan and
Steinberger, Josef and
Yangarber, Roman",
booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3711",
doi = "10.18653/v1/W19-3711",
pages = "83--88",
abstract = "This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60{\%} to 83{\%} in terms of Strict and Relaxed metrics, respectively.",
}
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%0 Conference Proceedings
%T TLR at BSNLP2019: A Multilingual Named Entity Recognition System
%A Moreno, Jose G.
%A Linhares Pontes, Elvys
%A Coustaty, Mickael
%A Doucet, Antoine
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F moreno-etal-2019-tlr
%X This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60% to 83% in terms of Strict and Relaxed metrics, respectively.
%R 10.18653/v1/W19-3711
%U https://aclanthology.org/W19-3711
%U https://doi.org/10.18653/v1/W19-3711
%P 83-88
Markdown (Informal)
[TLR at BSNLP2019: A Multilingual Named Entity Recognition System](https://aclanthology.org/W19-3711) (Moreno et al., BSNLP 2019)
ACL