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 …

Word representation has always been an important research area in the history of natural
language processing (NLP). Understanding such complex text data is imperative, given that …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

Sentiment analysis has been a well-studied research direction in computational linguistics.
Deep neural network models, including convolutional neural networks (CNN) and recurrent …

The rapidly developed Health 2.0 technology has provided people with more opportunities
to conduct online medical consultation than ever before. Understanding contexts within …

The rise of sophisticated black-box machine learning models in Artificial Intelligence
systems has prompted the need for explanation methods that reveal how these models work …

Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …

Real world scenarios present a challenge for text classification, since labels are usually
expensive and the data is often characterized by class imbalance. Active Learning (AL) is a …

Short text classification has found rich and critical applications in news and tweet tagging to
help users find relevant information. Due to lack of labeled training data in many practical …

In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …