We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Semi-supervised learning is a learning paradigm concerned with the study of how
computers and natural systems such as humans learn in the presence of both labeled and …

Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …

In traditional machine learning approaches to classification, one uses only a labeled set to
train the classifier. Labeled instances however are often difficult, expensive, or time …

Determining the intended sense of words in text-word sense disambiguation (WSD)-is a
long standing problem in natural language processing. Recently, researchers have shown …

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses)
of words in context, has been a long-standing research objective for natural language …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

In this paper, we present an overview of generalized expectation criteria (GE), a simple,
robust, scalable method for semi-supervised training using weakly-labeled data. GE fits …

The expansion of the Internet over the last decade and the proliferation of online social
communities, such as Facebook, Google+, and Twitter, as well as multimedia sharing sites …