Acerca de mí
Soy estudiante de doctorado en Ciencias de la Computación en Sorbonne Université y en el equipo de investigación ALMAnaCH en el Inria.
Trabajo principalmente en machine learning, deep learning y procesamiento de lenguaje natural.
Me encanta el café, las galletas y la programación.
Intereses
- Machine Learning
- Deep Learning
- Text Mining
- NLP
Educación
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Doctorado en Ciencias de la Computación
Sorbonne Université
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Pregrado MIASHS, 2018
Université Paris 8
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Master en Matemáticas, 2017
Aix-Marseille Université
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Pregrado en Matemáticas, 2016
Universidad Nacional de Colombia
Publicaciones destacadas
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
Publicaciones recientes
SinNer@CLEF-HIPE2020: Sinful Adaptation of SotA models for Named Entity Recognition in Historical French and German Newspapers
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.
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
CamemBERT: a Tasty French Language Model
We explore the impact of the training data size on a French version of RoBERTa.
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect.
Les modèles de langue contextuels Camembert pour le Français : impact de la taille et de l'hétérogénéité des données d'entrainement
We explore the impact of the training data size and heterogeneity on French language modeling.
Establishing a New State-of-the-Art for French Named Entity Recognition
We explore convert the NER annotations of the French TreeBank to a more user-friendly format and establish a new state of the art for French NER.
French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
We investigate the impact of different types and size of training corpora on language models.
Presentaciones recientes y próximas
Des Méthodes de TAL modernes pour l'Enrichissement de Documents
Nous présentons une pipeline pour le traitement et l’enrichissement de documents basée sur les dernières méthodes d’apprentissage neuronal.
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures
We propose a new pipeline to filter, clean and classify Common Crawl by language, we publish the final corpus under the name OSCAR.
