1 code implementation • SEMEVAL 2017 • Saif M. Mohammad, Felipe Bravo-Marquez
This paper examines the task of detecting intensity of emotion from text.
2 code implementations • LREC 2022 • José Cañete, Sebastián Donoso, Felipe Bravo-Marquez, Andrés Carvallo, Vladimir Araujo
In this paper we present ALBETO and DistilBETO, which are versions of ALBERT and DistilBERT pre-trained exclusively on Spanish corpora.
1 code implementation • EACL 2021 • Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer
To avoid the "meaning conflation deficiency" of word embeddings, a number of models have aimed to embed individual word senses.
2 code implementations • COLING 2022 • Matias Rojas, Felipe Bravo-Marquez, Jocelyn Dunstan
Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories.
Ranked #1 on Nested Named Entity Recognition on Chilean Waiting List (Micro F1 (Exact Span) metric)
1 code implementation • LREC 2022 • Vladimir Araujo, Andrés Carvallo, Souvik Kundu, José Cañete, Marcelo Mendoza, Robert E. Mercer, Felipe Bravo-Marquez, Marie-Francine Moens, Alvaro Soto
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years.
1 code implementation • 13 May 2022 • Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg
We present the first shared task on semantic change discovery and detection in Spanish and create the first dataset of Spanish words manually annotated for semantic change using the DURel framework (Schlechtweg et al., 2018).
1 code implementation • ACM Transactions on Computing for Healthcare 2022 • Pablo Báez, Felipe Bravo-Marquez, Jocelyn Dunstan, Matías Rojas, Fabián Villena
The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.
no code implementations • WS 2017 • Saif M. Mohammad, Felipe Bravo-Marquez
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet.
no code implementations • SEMEVAL 2018 • Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko
We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet.
no code implementations • ACL 2019 • David Trye, Andreea Calude, Felipe Bravo-Marquez, Te Taka Keegan
M{\=a}ori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the M{\=a}ori community.
no code implementations • SEMEVAL 2020 • Frank D. Zamora-Reina, Felipe Bravo-Marquez
We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another.
no code implementations • ACL 2021 • Jhonny Cerezo, Felipe Bravo-Marquez, Alexandre Henri Bergel
The quality of the annotated data directly influences in the success of supervised NLP models.
no code implementations • EACL (BEA) 2021 • Javier Muñoz, Felipe Bravo-Marquez
We present a new task in educational NLP, recommend the best interventions to help special needs education professionals to work with students with different disabilities.
no code implementations • LChange (ACL) 2022 • Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg
We present the first shared task on semantic change discovery and detection in Spanish.
no code implementations • 22 Dec 2023 • Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Ehsan Abbasnejad, Hamed Damirchi, Ignacio M. Jara, Felipe Bravo-Marquez, Anton Van Den Hengel
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning.