241 citations · 1.1k across the 49 of their papers we have counts for
50 papers
Context-aware Adversarial Attack on Named Entity Recognition
Shuguang Chen, Leonardo Neves, Thamar Solorio
In recent years, large pre-trained language models (PLMs) have achieved remarkable performance on many natural language processing benchmarks. Despite their success, prior studies…
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.…
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts
Asahi Ushio, Leonardo Neves, Vitor Silva +2
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-format…
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
Shuguang Chen, Leonardo Neves, Thamar Solorio
In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and di…
SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
Jiaxin Pei, Vítor Silva, Maarten Bos +4
We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese…
Twitter Topic Classification
Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados +3
Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A…