Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

BERT has achieved impressive performance in several NLP tasks. However, there has been
limited investigation on its adaptation guidelines in specialised domains. Here we focus on …

Laws and their interpretations, legal arguments and agreements\are typically expressed in
writing, leading to the production of vast corpora of legal text. Their analysis, which is at the …

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial
intelligence, especially natural language processing, to benefit tasks in the legal domain. In …

One concern with the rise of large language models lies with their potential for significant
harm, particularly from pretraining on biased, obscene, copyrighted, and private information …

Many specialized domains remain untouched by deep learning, as large labeled datasets
require expensive expert annotators. We address this bottleneck within the legal domain by …

Recently, a new paradigm of building general-purpose language models (eg, Google's Bert
and OpenAI's GPT-2) in Natural Language Processing (NLP) for text feature extraction, a …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …