Bernardo Stearns

dblp:229/4352 · DBLP profile ↗
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6ranked-venue papers
2as first author
4since 2021 · last 2026
0000-0001-9377-8572ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 2 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 3 since 2021Databases, data management, data science and information retrieval · 3 · 3 since 2021Human-computer interaction and ubiquitous computing · 1
YearPublicationVenuePosition
2026 Cross-Corpus CEFR Classification through Artificial Learners Perplexities
abstract
International audience
Bernardo Stearns, John P. McCrae, Thomas Gaillat
LREC1
2025 Cuaċ: Fast and Small Universal Representations of Corpora
abstract
The increasing size and diversity of corpora in natural language processing requires highly efficient processing frameworks. Building on the universal corpus format, Teanga, we present Cuaċ, a format for the compact representation of corpora. We describe this methodology based on short-string compression and indexing techniques and show that the files created with this methodology are similar to compressed human-readable serializations and can be further compressed using lossless compression. We also show that this introduces no computational penalty on the time to process files. This methodology aims to speed up natural language processing pipelines and is the basis for a fast database system for corpora.
John P. McCrae, Bernardo Stearns, Alamgir Munir Qazi, Shubhanker Banerjee, Atul Kr. Ojha
LDK2
2023 The Cardamom Workbench for Historical and Under-Resourced Languages
Adrian Doyle, Theodorus Fransen, Bernardo Stearns, John P. McCrae, Oksana Dereza, Priya Rani
LDK3
2023 A new learner language data set for the study of English for Specific Purposes at university
Cyriel Mallart, Nicolas Ballier, Jen-Yu Li, Andrew J. Simpkin, Bernardo Stearns, Rémi Venant, Thomas Gaillat
LDK5
2019 A Supervised Learning Model for the Automatic Assessment of Language Levels Based on Learner Errors
Nicolas Ballier, Thomas Gaillat, Andrew J. Simpkin, Bernardo Stearns, Manon Bouyé, Manel Zarrouk
EC-TEL4
2017 Scholar Performance Prediction using Boosted Regression Trees Techniques
Bernardo Stearns, Fábio Medeiros Rangel, Flavio Rangel, Fabrício Firmino de Faria, Jonice Oliveira
ESANN1