  Semi-supervised classification is an interesting idea where classification
models are learned from both labeled and unlabeled data. It has several
advantages over supervised classification in natural language processing
domain. For instance, supervised classification exploits only labeled data that
are expensive, often difficult to get, inadequate in quantity, and require
human experts for annotation. On the other hand, unlabeled data are inexpensive
and abundant. Despite the fact that many factors limit the wide-spread use of
semi-supervised classification, it has become popular since its level of
performance is empirically as good as supervised classification. This study
explores the possibilities and achievements as well as complexity and
limitations of semi-supervised classification for several natural langue
processing tasks like parsing, biomedical information processing, text
classification, and summarization.
