Recent advances in large pretrained language models have increased attention to zero-shot
text classification. In particular, models finetuned on natural language inference datasets …

Hierarchical text classification aims to categorize each document into a set of classes in a
label taxonomy. Most earlier works focus on fully or semi-supervised methods that require a …

The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning
(SSL) provides a promising way to leverage unlabeled data by pseudo labels. However …

Graph neural networks have triggered a resurgence of graph-based text classification
methods, defining today's state of the art. We show that a wide multi-layer perceptron (MLP) …

To reliably and accurately classify complicated" big" datasets, machine learning models
must be continually improved. This research proposes straightforward yet competitive neural …

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot
learners. However, most existing zero-shot methods involve heavy human engineering or …

Instead of relying on human-annotated training samples to build a classifier, weakly
supervised scientific paper classification aims to classify papers only using category …

The availability of source code has been put forward as one of the most critical factors for
improving the reproducibility of scientific research. This work studies trends in source code …

Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based
on minimal high-level human guidance, such as a few label-indicative seed words or …

Fully supervised log anomaly detection methods suffer the heavy burden of annotating
massive unlabeled log data. Recently, many semi-supervised methods have been proposed …