Bernardo Stearns

According to our database1, Bernardo Stearns authored at least 11 papers between 2017 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2025
Assessing the validity of new paradigmatic complexity measures as criterial features for proficiency in L2 writings in English.
CoRR, March, 2025

Cuaċ: Fast and Small Universal Representations of Corpora.
Proceedings of the 5th Conference on Language, Data and Knowledge, 2025

2024
Teanga Data Model for Linked Corpora.
Proceedings of the 9th Workshop on Linked Data in Linguistics, 2024

2023
A new learner language data set for the study of English for Specific Purposes at university.
Proceedings of the 4th Conference on Language, Data and Knowledge, 2023

The Cardamom Workbench for Historical and Under-Resourced Languages.
Proceedings of the 4th Conference on Language, Data and Knowledge, 2023

Exploring a New Grammatico-functional Type of Measure as Part of a Language Learning Expert System.
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications, 2023

2020
Un prototype en ligne pour la prédiction du niveau de compétence en anglais des productions écrites (A prototype for web-based prediction of English proficiency levels in writings).
Proceedings of the Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 2020

From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data.
Proceedings of the 1st International Workshop on Language Technology Platforms, 2020

2019
A Supervised Learning Model for the Automatic Assessment of Language Levels Based on Learner Errors.
Proceedings of the Transforming Learning with Meaningful Technologies, 2019

2018
Implicit and Explicit Aspect Extraction in Financial Microblogs.
Proceedings of the First Workshop on Economics and Natural Language Processing, 2018

2017
Scholar Performance Prediction using Boosted Regression Trees Techniques.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017


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