Mickael Coustaty


pdf bib
Dataset for Temporal Analysis of English-French Cognates
Esteban Frossard | Mickael Coustaty | Antoine Doucet | Adam Jatowt | Simon Hengchen
Proceedings of the 12th Language Resources and Evaluation Conference

Languages change over time and, thanks to the abundance of digital corpora, their evolutionary analysis using computational techniques has recently gained much research attention. In this paper, we focus on creating a dataset to support investigating the similarity in evolution between different languages. We look in particular into the similarities and differences between the use of corresponding words across time in English and French, two languages from different linguistic families yet with shared syntax and close contact. For this we select a set of cognates in both languages and study their frequency changes and correlations over time. We propose a new dataset for computational approaches of synchronized diachronic investigation of language pairs, and subsequently show novel findings stemming from the cognate-focused diachronic comparison of the two chosen languages. To the best of our knowledge, the present study is the first in the literature to use computational approaches and large data to make a cross-language diachronic analysis.


pdf bib
TLR at BSNLP2019: A Multilingual Named Entity Recognition System
Jose G. Moreno | Elvys Linhares Pontes | Mickael Coustaty | Antoine Doucet
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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.