activity
20172024
most citedT-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

61 citations · 177 across the 13 of their papers we have counts for

collaborators

28 papers

cs.CL2024

Multilingual Topic Classification in X: Dataset and Analysis

Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri +1

In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this conte…

cs.CL20225 cited

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…

cs.CL202218 cited

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…

cs.CL202213 cited

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…

cs.CL202261 cited

T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

Asahi Ushio, Jose Camacho-Collados

Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transfor…

cs.CL2022

Assessing the Limits of the Distributional Hypothesis in Semantic Spaces: Trait-based Relational Knowledge and the Impact of Co-occurrences

Mark Anderson, Jose Camacho-Collados

The increase in performance in NLP due to the prevalence of distributional models and deep learning has brought with it a reciprocal decrease in interpretability. This has spurred…