  Text classification is a task of automatic classification of text into one of
the predefined categories. The problem of text classification has been widely
studied in different communities like natural language processing, data mining
and information retrieval. Text classification is an important constituent in
many information management tasks like topic identification, spam filtering,
email routing, language identification, genre classification, readability
assessment etc. The performance of text classification improves notably when
phrase patterns are used. The use of phrase patterns helps in capturing
non-local behaviours and thus helps in the improvement of text classification
task. Phrase structure extraction is the first step to continue with the phrase
pattern identification. In this survey, detailed study of phrase structure
learning methods have been carried out. This will enable future work in several
NLP tasks, which uses syntactic information from phrase structure like grammar
checkers, question answering, information extraction, machine translation, text
classification. The paper also provides different levels of classification and
detailed comparison of the phrase structure learning methods.
