27 citations · 94 across the 10 of their papers we have counts for
11 papers
Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter
Daniel Loureiro, Kiamehr Rezaee, Talayeh Riahi +4
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.…
Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary
Daniel Loureiro, Alípio Mário Jorge
Progress on commonsense reasoning is usually measured from performance improvements on Question Answering tasks designed to require commonsense knowledge. However, fine-tuning larg…
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
Daniel Loureiro, Aminette D'Souza, Areej Nasser Muhajab +6
Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it chall…
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi +11
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic foc…
TimeLMs: Diachronic Language Models from Twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves +2
Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specializ…
LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond
Daniel Loureiro, Alípio Mário Jorge, Jose Camacho-Collados
Distributional semantics based on neural approaches is a cornerstone of Natural Language Processing, with surprising connections to human meaning representation as well. Recent Tra…
Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning
Sofia Oliveira, Daniel Loureiro, Alípio Jorge
The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed fo…
Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation
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 coverag…
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation
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 t…
LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
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. Our solution is based on a novel system for Word Se…
Affordance Extraction and Inference based on Semantic Role Labeling
Daniel Loureiro, Alípio Mário Jorge
Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain w…