Representing words as numerical vectors based on the contexts in which they appear has
become the de facto method of analyzing text with machine learning. In this paper, we …

Distributed vector representations or embeddings map variable length text to dense fixed
length vectors as well as capture prior knowledge which can transferred to downstream …

Current text classification methods typically require a good number of human-labeled
documents as training data, which can be costly and difficult to obtain in real applications …

Context: Requirements engineering (RE) researchers have been experimenting with
machine learning (ML) and deep learning (DL) approaches for a range of RE tasks, such as …

Insufficient or even unavailable training data of emerging classes is a big challenge of many
classification tasks, including text classification. Recognising text documents of classes that …

Zero-shot learning has been a tough problem since no labeled data is available for unseen
classes during training, especially for classes with low similarity. In this situation, transferring …

How can we extend a pre-trained model to many language understanding tasks, without
labeled or additional unlabeled data? Pre-trained language models (PLMs) have been …

Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of
classes from a class hierarchy. Most existing HMTC methods train classifiers using massive …

Hierarchical attention networks have recently achieved remarkable performance for
document classification in a given language. However, when multilingual document …

Text classification of unseen classes is a challenging Natural Language Processing task
and is mainly attempted using two different types of approaches. Similarity-based …