Download Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins PDF

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By Jacob Perkins

This ebook is meant for Python programmers drawn to studying find out how to do normal language processing. perhaps you’ve discovered the bounds of standard expressions the not easy means, or you’ve learned that human language can't be deterministically parsed like a working laptop or computer language. maybe you could have extra textual content than you recognize what to do with, and wish computerized how one can examine and constitution that textual content. This Cookbook will allow you to educate and use statistical language versions to strategy textual content in ways in which are virtually most unlikely with commonplace programming instruments. A simple wisdom of Python and the fundamental textual content processing innovations is anticipated. a few adventure with commonplace expressions may also be worthwhile.

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48 3 Creating Custom Corpora In this chapter, we will cover the following recipes: ff Setting up a custom corpus ff Creating a wordlist corpus ff Creating a part-of-speech tagged word corpus ff Creating a chunked phrase corpus ff Creating a categorized text corpus ff Creating a categorized chunk corpus reader ff Lazy corpus loading ff Creating a custom corpus view ff Creating a MongoDB-backed corpus reader ff Corpus editing with file locking Introduction In this chapter, we'll cover how to use corpus readers and create custom corpora.

The en_GB dictionary Always ensure that you use the correct dictionary for whichever language you are performing spelling correction on. The en_US dictionary can give you different results than en_GB, such as for the word theater. replace('theater') 'theatre' Personal word lists Enchant also supports personal word lists. These can be combined with an existing dictionary, allowing you to augment the dictionary with your own words. txt that had nltk on one line. replace('nltk') 'nltk' See also The previous recipe covered an extreme form of spelling correction by replacing repeating characters.

However, computers don't know that "looooooove" is a variation of "love" unless they are told. This recipe presents a method to remove these annoying repeating characters in order to end up with a proper English word. Getting ready As in the previous recipe, we will be making use of the re module, and more specifically, backreferences. A backreference is a way to refer to a previously matched group in a regular expression. This will allow us to match and remove repeating characters. How to do it...

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