3 citations · 7 across the 3 of their papers we have counts for
7 papers · 1 filter
RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams
Gabriel Iturra-Bocaz, Felipe Bravo-Marquez
Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answ…
LSCDiscovery: A shared task on semantic change discovery and detection in Spanish
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 th…
Evaluation Benchmarks for Spanish Sentence Representations
Vladimir Araujo, Andrés Carvallo, Souvik Kundu +6
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years. This fact implies the need for resources to evaluate…
ALBETO and DistilBETO: Lightweight Spanish Language Models
José Cañete, Sebastián Donoso, Felipe Bravo-Marquez +2
In recent years there have been considerable advances in pre-trained language models, where non-English language versions have also been made available. Due to their increasing use…
PolyLM: Learning about Polysemy through Language Modeling
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. These methods at one time performed well on tasks su…
WASSA-2017 Shared Task on Emotion Intensity
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. We create the first datasets of tweets annotated for anger, fear, joy, and sa…
Emotion Intensities in Tweets
Saif M. Mohammad, Felipe Bravo-Marquez
This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a t…