Industrial organizations generate massive volumes of data during their routine business and
production activities. Such data may be structured (numerical or categorical), or it may be …

Supervised learning algorithms employ labeled training data for classification purposes
while obtaining labeled data for large datasets is costly and time consuming. Semi …

Abstract Vector Space Models (VSM) are commonly used in language processing to
represent certain aspects of natural language semantics. Semantics of VSM comes from the …

Most recent neural semi-supervised learning (SSL) algorithms rely on adding small
perturbation to either the input vectors or their representations. These methods have been …

In automated text categorization, given a small number of labeled documents, it is very
challenging, if not impossible, to build a reliable classifier that is able to achieve high …

Text classification is a sophisticated field of research in natural language processing that
deals with the problem of automatically classifying new documents into pre-defined classes …

A huge amount of data is generated daily leading to big data challenges. One of them is
related to text mining, especially text classification. To perform this task we usually need a …

Weakly-supervised text classification has received much attention in recent years for it can
alleviate the heavy burden of annotating massive data. Among them, keyword-driven …

As the amount of information available on the internet grows at a rapid pace, text
classification becomes critical. This data is in an unstructured state and will need to be …

Abstract In the Semi-Supervised Text Classification (SSTC) task, the performance of the
SSTC-based models heavily rely on the accuracy of the pseudo-labels for unlabeled data …