Language Models for Text Classification: Is In-Context Learning Enough?

no code implementations • 26 Mar 2024 • Aleksandra Edwards, Jose Camacho-Collados

This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.

In-Context Learning text-classification +2

Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification

no code implementations • 17 Nov 2021 • Aleksandra Edwards, Asahi Ushio, Jose Camacho-Collados, Hélène de Ribaupierre, Alun Preece

Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity.

Active Learning Data Augmentation +2

Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification

no code implementations • COLING 2020 • Aleksandra Edwards, Jose Camacho-Collados, H{\'e}l{\`e}ne De Ribaupierre, Alun Preece

Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification.

Language Modelling text-classification +2

Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports

no code implementations • 27 Oct 2020 • Aleksandra Edwards, David Rogers, Jose Camacho-Collados, Hélène de Ribaupierre, Alun Preece

The task of text and sentence classification is associated with the need for large amounts of labelled training data.

Classification General Classification +2