Summary

A large proportion of information in the digital world is textual. Text mining and NLP are areas concerned with extracting information from this unstructured form of data. Several important sub areas in the field are active topics of research today and an understanding of these areas is essential for data scientists.

Text categorization is concerned with classifying documents into pre-determined categories. Text may be enriched by annotating words, as with POS tagging, in order to give it more structure for subsequent processing tasks to act on. Unsupervised techniques such as clustering can be applied to documents as well. Information extraction and named entity recognition help identify information-rich specifics such as location, person or organization name, and so on. Summarization is another important application for producing concise abstracts of larger documents or sets of documents. Various ambiguities of language and semantics such as context, word sense, and reasoning make...