no code implementations • EMNLP (MRL) 2021 • Kiamehr Rezaee, Daniel Loureiro, Jose Camacho-Collados, Mohammad Taher Pilehvar
In this paper we analyze the extent to which contextualized sense embeddings, i. e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages. To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation.
no code implementations • 4 Aug 2023 • Daniel Loureiro, Kiamehr Rezaee, Talayeh Riahi, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-Collados
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.
1 code implementation • 12 Oct 2022 • Daniel Loureiro, Alípio Mário Jorge
However, this approach is restricted by the LM's vocabulary available for masked predictions, and its precision is subject to the context provided by the assertion.
1 code implementation • COLING 2022 • Daniel Loureiro, Aminette D'Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift.
1 code implementation • 29 Jun 2022 • Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, Gonzalo Medina, Thomas Buhrmann, Leonardo Neves, Francesco Barbieri
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media.
2 code implementations • ACL 2022 • Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-Collados
Despite its importance, the time variable has been largely neglected in the NLP and language model literature.
1 code implementation • 26 May 2021 • Daniel Loureiro, Alípio Mário Jorge, Jose Camacho-Collados
Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task.
1 code implementation • 4 Jan 2021 • Sofia Oliveira, Daniel Loureiro, Alípio Jorge
The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling.
1 code implementation • CL (ACL) 2021 • Daniel Loureiro, Kiamehr Rezaee, Mohammad Taher Pilehvar, Jose Camacho-Collados
We also perform an in-depth comparison of the two main language model based WSD strategies, i. e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data.
1 code implementation • EMNLP 2020 • Daniel Loureiro, Jose Camacho-Collados
State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models.
1 code implementation • ACL 2019 • Daniel Loureiro, Alipio Jorge
Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings.
1 code implementation • WS 2019 • Daniel Loureiro, Alipio Jorge
This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5.
no code implementations • WS 2018 • Daniel Loureiro, Alípio Mário Jorge
Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing.