Search Results for author: Felipe Bravo-Marquez

Found 15 papers, 7 papers with code

Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition

1 code implementation 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.

named-entity-recognition NER +1

Interventions Recommendation: Professionals’ Observations Analysis in Special Needs Education

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.

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

1 code implementation13 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).

Change Detection

ALBETO and DistilBETO: Lightweight Spanish Language Models

1 code implementation 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.

Natural Language Understanding NER +1

PolyLM: Learning about Polysemy through Language Modeling

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.

Language Modelling Word Embeddings +1

DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings

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.

Change Detection Word Embeddings

M\=aOri Loanwords: A Corpus of New Zealand English Tweets

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.

SemEval-2018 Task 1: Affect in Tweets

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.

Classification Emotion Classification +2

Emotion Intensities in Tweets

1 code implementation SEMEVAL 2017 Saif M. Mohammad, Felipe Bravo-Marquez

This paper examines the task of detecting intensity of emotion from text.

regression

WASSA-2017 Shared Task on Emotion Intensity

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.