Work

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

Year: 2019

Type: conference-paper

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.

Source: Workshop on Balto-Slavic Natural Language Processing (BSNLP)

DOI: 10.18653/v1/w19-3711

Authors: Jose G. Moreno, Elvys Linhares Pontes, Mickael Coustaty, Antoine Doucet