AI tools are increasingly deployed in community contexts. However, datasets used to
evaluate AI are typically created by developers and annotators outside a given community …

Purpose The majority of existing studies about named entity recognition (NER) concentrate
on the prediction enhancement of deep neural network (DNN)-based models themselves …

How to better reduce measurement variability and bias introduced by subjectivity in
crowdsourced labelling remains an open question. We introduce a theoretical framework for …

With the rapid development of crowdsourcing learning, amount of labels can be obtained
from crowd workers fast and cheaply. However, crowdsourcing learning also faces …

Large language models (LLMs) stand to reshape traditional methods of working with data.
While LLMs unlock new and potentially useful ways of interfacing with data, their use in …

Many sequential classification tasks are affected by label noise that varies over time. Such
noise can cause label quality to improve, worsen, or periodically change over time. We first …

Crowdsourcing has been playing an essential role in machine learning since it can obtain a
large number of labels in an economical and fast manner for training increasingly complex …

Inverse Synthetic Aperture Radar (ISAR) is a widespread imaging technology used to
characterise and classify non-cooperative targets. Unfortunately, the quality of ISAR imagery …

Many sequential classification tasks are affected by label noise that changes over time. Such
noise might arise from label quality improving, worsening, or periodically changing over …