November 2016
Beginner to intermediate
687 pages
15h 31m
English
book
Natural Language Processing: Python and NLTK
by Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur
Content preview from Natural Language Processing: Python and NLTKBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Developing a chunker using pos-tagged corpora
Chunking is the process used to perform entity detection. It is used for the segmentation and labeling of multiple sequences of tokens in a sentence.
To design a chunker, a chunk grammar should be defined. A chunk grammar holds the rules of how chunking should be done.
Let's consider the example that performs Noun Phrase Chunking by forming the chunk rules:
>>> import nltk >>> sent=[("A","DT"),("wise", "JJ"), ("small", "JJ"),("girl", "NN"), ("of", "IN"), ("village", "N"), ("became", "VBD"), ("leader", "NN")] >>> sent=[("A","DT"),("wise", "JJ"), ("small", "JJ"),("girl", "NN"), ("of", "IN"), ("village", "NN"), ("became", "VBD"), ("leader", "NN")] >>> grammar = "NP: {<DT>?<JJ>*<NN><IN>?<NN>*}" >>> find = ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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