Semi-supervised text classification uses both labeled and unlabeled data to construct
classifiers. The key issue is how to utilize the unlabeled data. Clustering based classification …

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 …

Usually, semi-supervised learning requires a number of prior knowledge to supervise the
learning process, such as, seeds in Seeded-Kmeans, pair-wise constraints in COP-Kmeans …

Most text categorization methods require text content of documents that is often difficult to
obtain. We consider “Collaborative Text Categorization”, where each document is …

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

Text categorization is one of the fundamental tasks in text mining. Classical supervised
methods need lot of labeled data to train a classifier. Since assigning labels to the large …

The vast amount of electronic documents available on the Internet demands for automatic
tools that help people finding, organizing and easily accessing to all this information …

In the standard EM-based semi-supervised text classification, the classification performance
is not well when the initial labeled samples are a few. How to improve the performance is an …

Many semi-supervised learning algorithms only consider the distribution of word frequency,
ignoring the semantic and syntactic information underlying the documents. In this paper, we …

Although semi-supervised variational autoencoder (SemiVAE) works in image classification
task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective …