GETTING STARTED ON NATURAL LANGUAGE PROCESSING WITH PYTHON

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Download free Getting Started on Natural Language Processing with Python.pdf The term Natural Language Processing encompasses a broad set of techniques for automated generation, manipulation and analysis of natural or human languages. Although most NLP techniques inherit largely from Linguistics and Artificial Intelligence, they are also influenced by relatively newer areas such as Machine Learning, Computational Statistics and Cognitive Science.

Before we see some examples of NLP techniques, it will be useful to introduce some very basic terminology. Please note that as a side effect of keeping things simple, these definitions may not stand up to strict linguistic scrutiny.

Token: Before any real processing can be done on the input text, it needs to be segmented into linguistic units such as words, punctuation, numbers or alphanumerics. These units are known as tokens.
Sentence: An ordered sequence of tokens.
Tokenization: The process of splitting a sentence into its constituent tokens. For segmented languages such as English, the existence of whitespace makes tokenization relatively easier and uninteresting. However, for languages such as Chinese and Arabic, the task is more difficult since there are no explicit boundaries. Furthermore, almost all characters in such non-segmented languages can exist as one-character words by themselves but can also join together to form multi-character words.

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