Spell correction
It is not a necessary to use a spellchecker for all NLP applications, but some use cases require you to use a basic spellcheck. We can create a very basic spellchecker by just using a dictionary lookup. There are some enhanced string algorithms that have been developed for fuzzy string matching. One of the most commonly used is edit-distance. NLTK also provides you with a variety of metrics module that has edit_distance.
>>>from nltk.metrics import edit_distance >>>edit_distance("rain","shine") 3
We will cover this module in more detail in advanced chapters. We also have one of the most elegant codes for spellchecker from Peter Norvig, which is quite easy to understand and written in pure Python.
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