With advanced digitalisation, we can observe a massive increase of user-generated content
on the web that provides opinions of people on different subjects. Sentiment analysis is the …

In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

Self-training methods have gained significant attention in recent years due to their
effectiveness in leveraging small labeled datasets and large unlabeled observations for …

Natural language understanding comprises a wide range of diverse tasks such as textual
entailment, question answering, semantic similarity assessment, and document …

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an
active topic in computer vision but lacks a theoretical foundation. We first extend existing …

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a
number of standard benchmark datasets for graph-based semi-supervised learning …

Much effort has been devoted to evaluate whether multi-task learning can be leveraged to
learn rich representations that can be used in various Natural Language Processing (NLP) …

Text classification is the most vital area in natural language processing in which text data is
automatically sorted into a predefined set of classes. The application of text classification is …

Context Recent developments in requirements engineering (RE) methods have seen a
surge in using machine-learning (ML) algorithms to solve some difficult RE problems. One …