Tagging POS (6.1.4)

FreqDist.inc() is new for me. According to help, “Increment this FreqDist’s count for the given sample.” Okay, then start from empty and extract last 1, 2, 3 chars from the words.

>>> from nltk.corpus import brown
>>> suffix_fdist = nltk.FreqDist()
>>> for word in brown.words():
...     word = word.lower()
...     suffix_fdist.inc(word[-1:])
...     suffix_fdist.inc(word[-2:])
...     suffix_fdist.inc(word[-3:])
... 
>>> common_suffixes = suffix_fdist.keys()[:100]
>>> print common_suffixes
['e', ',', '.', 's', 'd', 't', 'he', 'n', 'a', 'of', 'the', 'y', 'r', 'to', 'in', 'f', 'o', 'ed', 'nd', 'is', 'on', 'l', 'g', 'and', 'ng', 'er', 'as', 'ing', 'h', 'at', 'es', 'or', 're', 'it', '``', 'an', "''", 'm', ';', 'i', 'ly', 'ion', 'en', 'al', '?', 'nt', 'be', 'hat', 'st', 'his', 'th', 'll', 'le', 'ce', 'by', 'ts', 'me', 've', "'", 'se', 'ut', 'was', 'for', 'ent', 'ch', 'k', 'w', 'ld', '`', 'rs', 'ted', 'ere', 'her', 'ne', 'ns', 'ith', 'ad', 'ry', ')', '(', 'te', '--', 'ay', 'ty', 'ot', 'p', 'nce', "'s", 'ter', 'om', 'ss', ':', 'we', 'are', 'c', 'ers', 'uld', 'had', 'so', 'ey']
>>> def pos_features(word):
...     features = {}
...     for suffix in common_suffixes:
...             features['endswith(%s)' % suffix] = word.lower().endswith(suffix)
...     return features
... 
>>> tagged_words = brown.tagged_words(categories='news')
>>> featuresets = [(pos_features(n), g) for (n, g) in tagged_words]
>>> size = int(len(featuresets) * 0.1)
>>> train_set, test_set = featuresets[size:], featuresets[:size]
>>> classifier = nltk.DecisionTreeClassifier.train(train_set)

The last step takes extremely long time. I killed the session after 10 minutes. I had to skip remaining part of this section.

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