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16Jun/1039

Text Classification for Sentiment Analysis – Eliminate Low Information Features

When your classification model has hundreds or thousands of features, as is the case for text categorization, it's a good bet that many (if not most) of the features are low information. These are features that are common across all classes, and therefore contribute little information to the classification process. Individually they are harmless, but in aggregate, low information features can decrease performance.

Eliminating low information features gives your model clarity by removing noisy data. It can save you from overfitting and the curse of dimensionality. When you use only the higher information features, you can increase performance while also decreasing the size of the model, which results in less memory usage along with faster training and classification. Removing features may seem intuitively wrong, but wait till you see the results.

High Information Feature Selection

Using the same evaluate_classifier method as in the previous post on classifying with bigrams, I got the following results using the 10000 most informative words:

evaluating best word features
accuracy: 0.93
pos precision: 0.890909090909
pos recall: 0.98
neg precision: 0.977777777778
neg recall: 0.88
Most Informative Features
             magnificent = True              pos : neg    =     15.0 : 1.0
             outstanding = True              pos : neg    =     13.6 : 1.0
               insulting = True              neg : pos    =     13.0 : 1.0
              vulnerable = True              pos : neg    =     12.3 : 1.0
               ludicrous = True              neg : pos    =     11.8 : 1.0
                  avoids = True              pos : neg    =     11.7 : 1.0
             uninvolving = True              neg : pos    =     11.7 : 1.0
              astounding = True              pos : neg    =     10.3 : 1.0
             fascination = True              pos : neg    =     10.3 : 1.0
                 idiotic = True              neg : pos    =      9.8 : 1.0

Contrast this with the results from the first article on classification for sentiment analysis, where we use all the words as features:

evaluating single word features
accuracy: 0.728
pos precision: 0.651595744681
pos recall: 0.98
neg precision: 0.959677419355
neg recall: 0.476
Most Informative Features
         magnificent = True              pos : neg    =     15.0 : 1.0
         outstanding = True              pos : neg    =     13.6 : 1.0
           insulting = True              neg : pos    =     13.0 : 1.0
          vulnerable = True              pos : neg    =     12.3 : 1.0
           ludicrous = True              neg : pos    =     11.8 : 1.0
              avoids = True              pos : neg    =     11.7 : 1.0
         uninvolving = True              neg : pos    =     11.7 : 1.0
          astounding = True              pos : neg    =     10.3 : 1.0
         fascination = True              pos : neg    =     10.3 : 1.0
             idiotic = True              neg : pos    =      9.8 : 1.0

The accuracy is over 20% higher when using only the best 10000 words and pos precision has increased almost 24% while neg recall improved over 40%. These are huge increases with no reduction in pos recall and even a slight increase in neg precision. Here's the full code I used to get these results, with an explanation below.

import collections, itertools
import nltk.classify.util, nltk.metrics
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews, stopwords
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures
from nltk.probability import FreqDist, ConditionalFreqDist

def evaluate_classifier(featx):
	negids = movie_reviews.fileids('neg')
	posids = movie_reviews.fileids('pos')

	negfeats = [(featx(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
	posfeats = [(featx(movie_reviews.words(fileids=[f])), 'pos') for f in posids]

	negcutoff = len(negfeats)*3/4
	poscutoff = len(posfeats)*3/4

	trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
	testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]

	classifier = NaiveBayesClassifier.train(trainfeats)
	refsets = collections.defaultdict(set)
	testsets = collections.defaultdict(set)

	for i, (feats, label) in enumerate(testfeats):
			refsets[label].add(i)
			observed = classifier.classify(feats)
			testsets[observed].add(i)

	print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
	print 'pos precision:', nltk.metrics.precision(refsets['pos'], testsets['pos'])
	print 'pos recall:', nltk.metrics.recall(refsets['pos'], testsets['pos'])
	print 'neg precision:', nltk.metrics.precision(refsets['neg'], testsets['neg'])
	print 'neg recall:', nltk.metrics.recall(refsets['neg'], testsets['neg'])
	classifier.show_most_informative_features()

def word_feats(words):
	return dict([(word, True) for word in words])

print 'evaluating single word features'
evaluate_classifier(word_feats)

word_fd = FreqDist()
label_word_fd = ConditionalFreqDist()

for word in movie_reviews.words(categories=['pos']):
	word_fd.inc(word.lower())
	label_word_fd['pos'].inc(word.lower())

for word in movie_reviews.words(categories=['neg']):
	word_fd.inc(word.lower())
	label_word_fd['neg'].inc(word.lower())

# n_ii = label_word_fd[label][word]
# n_ix = word_fd[word]
# n_xi = label_word_fd[label].N()
# n_xx = label_word_fd.N()

pos_word_count = label_word_fd['pos'].N()
neg_word_count = label_word_fd['neg'].N()
total_word_count = pos_word_count + neg_word_count

word_scores = {}

for word, freq in word_fd.iteritems():
	pos_score = BigramAssocMeasures.chi_sq(label_word_fd['pos'][word],
		(freq, pos_word_count), total_word_count)
	neg_score = BigramAssocMeasures.chi_sq(label_word_fd['neg'][word],
		(freq, neg_word_count), total_word_count)
	word_scores[word] = pos_score + neg_score

best = sorted(word_scores.iteritems(), key=lambda (w,s): s, reverse=True)[:10000]
bestwords = set([w for w, s in best])

def best_word_feats(words):
	return dict([(word, True) for word in words if word in bestwords])

print 'evaluating best word features'
evaluate_classifier(best_word_feats)

def best_bigram_word_feats(words, score_fn=BigramAssocMeasures.chi_sq, n=200):
	bigram_finder = BigramCollocationFinder.from_words(words)
	bigrams = bigram_finder.nbest(score_fn, n)
	d = dict([(bigram, True) for bigram in bigrams])
	d.update(best_word_feats(words))
	return d

print 'evaluating best words + bigram chi_sq word features'
evaluate_classifier(best_bigram_word_feats)

Calculating Information Gain

To find the highest information features, we need to calculate information gain for each word. Information gain for classification is a measure of how common a feature is in a particular class compared to how common it is in all other classes. A word that occurs primarily in positive movie reviews and rarely in negative reviews is high information. For example, the presence of the word "magnificent" in a movie review is a strong indicator that the review is positive. That makes "magnificent" a high information word. Notice that the most informative features above did not change. That makes sense because the point is to use only the most informative features and ignore the rest.

One of the best metrics for information gain is chi square. NLTK includes this in the BigramAssocMeasures class in the metrics package. To use it, first we need to calculate a few frequencies for each word: its overall frequency and its frequency within each class. This is done with a FreqDist for overall frequency of words, and a ConditionalFreqDist where the conditions are the class labels. Once we have those numbers, we can score words with the BigramAssocMeasures.chi_sq function, then sort the words by score and take the top 10000. We then put these words into a set, and use a set membership test in our feature selection function to select only those words that appear in the set. Now each file is classified based on the presence of these high information words.

Signficant Bigrams

The code above also evaluates the inclusion of 200 significant bigram collocations. Here are the results:

evaluating best words + bigram chi_sq word features
accuracy: 0.92
pos precision: 0.913385826772
pos recall: 0.928
neg precision: 0.926829268293
neg recall: 0.912
Most Informative Features
             magnificent = True              pos : neg    =     15.0 : 1.0
             outstanding = True              pos : neg    =     13.6 : 1.0
               insulting = True              neg : pos    =     13.0 : 1.0
              vulnerable = True              pos : neg    =     12.3 : 1.0
       ('matt', 'damon') = True              pos : neg    =     12.3 : 1.0
          ('give', 'us') = True              neg : pos    =     12.3 : 1.0
               ludicrous = True              neg : pos    =     11.8 : 1.0
             uninvolving = True              neg : pos    =     11.7 : 1.0
                  avoids = True              pos : neg    =     11.7 : 1.0
    ('absolutely', 'no') = True              neg : pos    =     10.6 : 1.0

This shows that bigrams don't matter much when using only high information words. In this case, the best way to evaluate the difference between including bigrams or not is to look at precision and recall. With the bigrams, you we get more uniform performance in each class. Without bigrams, precision and recall are less balanced. But the differences may depend on your particular data, so don't assume these observations are always true.

Improving Feature Selection

The big lesson here is that improving feature selection will improve your classifier. Reducing dimensionality is one of the single best things you can do to improve classifier performance. It's ok to throw away data if that data is not adding value. And it's especially recommended when that data is actually making your model worse.

24May/1038

Text Classification for Sentiment Analysis – Stopwords and Collocations

Improving feature extraction can often have a significant positive impact on classifier accuracy (and precision and recall). In this article, I'll be evaluating two modifications of the word_feats feature extraction method:

  1. filter out stopwords
  2. include bigram collocations

To do this effectively, we'll modify the previous code so that we can use an arbitrary feature extractor function that takes the words in a file and returns the feature dictionary. As before, we'll use these features to train a Naive Bayes Classifier.

import collections
import nltk.classify.util, nltk.metrics
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews

def evaluate_classifier(featx):
	negids = movie_reviews.fileids('neg')
	posids = movie_reviews.fileids('pos')

	negfeats = [(featx(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
	posfeats = [(featx(movie_reviews.words(fileids=[f])), 'pos') for f in posids]

	negcutoff = len(negfeats)*3/4
	poscutoff = len(posfeats)*3/4

	trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
	testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]

	classifier = NaiveBayesClassifier.train(trainfeats)
	refsets = collections.defaultdict(set)
	testsets = collections.defaultdict(set)

	for i, (feats, label) in enumerate(testfeats):
			refsets[label].add(i)
			observed = classifier.classify(feats)
			testsets[observed].add(i)

	print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
	print 'pos precision:', nltk.metrics.precision(refsets['pos'], testsets['pos'])
	print 'pos recall:', nltk.metrics.recall(refsets['pos'], testsets['pos'])
	print 'neg precision:', nltk.metrics.precision(refsets['neg'], testsets['neg'])
	print 'neg recall:', nltk.metrics.recall(refsets['neg'], testsets['neg'])
	classifier.show_most_informative_features()

Baseline Bag of Words Feature Extraction

Here's the baseline feature extractor for bag of words feature selection.

def word_feats(words):
	return dict([(word, True) for word in words])

evaluate_classifier(word_feats)

The results are the same as in the previous articles, but I've included them here for reference:

accuracy: 0.728
pos precision: 0.651595744681
pos recall: 0.98
neg precision: 0.959677419355
neg recall: 0.476
Most Informative Features
         magnificent = True              pos : neg    =     15.0 : 1.0
         outstanding = True              pos : neg    =     13.6 : 1.0
           insulting = True              neg : pos    =     13.0 : 1.0
          vulnerable = True              pos : neg    =     12.3 : 1.0
           ludicrous = True              neg : pos    =     11.8 : 1.0
              avoids = True              pos : neg    =     11.7 : 1.0
         uninvolving = True              neg : pos    =     11.7 : 1.0
          astounding = True              pos : neg    =     10.3 : 1.0
         fascination = True              pos : neg    =     10.3 : 1.0
             idiotic = True              neg : pos    =      9.8 : 1.0

Stopword Filtering

Stopwords are words that are generally considered useless. Most search engines ignore these words because they are so common that including them would greatly increase the size of the index without improving precision or recall. NLTK comes with a stopwords corpus that includes a list of 128 english stopwords. Let's see what happens when we filter out these words.

from nltk.corpus import stopwords
stopset = set(stopwords.words('english'))

def stopword_filtered_word_feats(words):
	return dict([(word, True) for word in words if word not in stopset])

evaluate_classifier(stopword_filtered_word_feats)

And the results for a stopword filtered bag of words are:

accuracy: 0.726
pos precision: 0.649867374005
pos recall: 0.98
neg precision: 0.959349593496
neg recall: 0.472

Accuracy went down .2%, and pos precision and neg recall dropped as well! Apparently stopwords add information to sentiment analysis classification. I did not include the most informative features since they did not change.

Bigram Collocations

As mentioned at the end of the article on precision and recall, it's possible that including bigrams will improve classification accuracy. The hypothesis is that people say things like "not great", which is a negative expression that the bag of words model could interpret as positive since it sees "great" as a separate word.

To find significant bigrams, we can use nltk.collocations.BigramCollocationFinder along with nltk.metrics.BigramAssocMeasures. The BigramCollocationFinder maintains 2 internal FreqDists, one for individual word frequencies, another for bigram frequencies. Once it has these frequency distributions, it can score individual bigrams using a scoring function provided by BigramAssocMeasures, such chi-square. These scoring functions measure the collocation correlation of 2 words, basically whether the bigram occurs about as frequently as each individual word.

import itertools
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures

def bigram_word_feats(words, score_fn=BigramAssocMeasures.chi_sq, n=200):
	bigram_finder = BigramCollocationFinder.from_words(words)
	bigrams = bigram_finder.nbest(score_fn, n)
	return dict([(ngram, True) for ngram in itertools.chain(words, bigrams)])

evaluate_classifier(bigram_word_feats)

After some experimentation, I found that using the 200 best bigrams from each file produced great results:

accuracy: 0.816
pos precision: 0.753205128205
pos recall: 0.94
neg precision: 0.920212765957
neg recall: 0.692
Most Informative Features
         magnificent = True              pos : neg    =     15.0 : 1.0
         outstanding = True              pos : neg    =     13.6 : 1.0
           insulting = True              neg : pos    =     13.0 : 1.0
          vulnerable = True              pos : neg    =     12.3 : 1.0
   ('matt', 'damon') = True              pos : neg    =     12.3 : 1.0
      ('give', 'us') = True              neg : pos    =     12.3 : 1.0
           ludicrous = True              neg : pos    =     11.8 : 1.0
         uninvolving = True              neg : pos    =     11.7 : 1.0
              avoids = True              pos : neg    =     11.7 : 1.0
('absolutely', 'no') = True              neg : pos    =     10.6 : 1.0

Yes, you read that right, Matt Damon is apparently one of the best predictors for positive sentiment in movie reviews. But despite this chuckle-worthy result

  • accuracy is up almost 9%
  • pos precision has increased over 10% with only 4% drop in recall
  • neg recall has increased over 21% with just under 4% drop in precision

So it appears that the bigram hypothesis is correct, and including significant bigrams can increase classifier effectiveness. Note that it's significant bigrams that enhance effectiveness. I tried using nltk.util.bigrams to include all bigrams, and the results were only a few points above baseline. This points to the idea that including only significant features can improve accuracy compared to using all features. In a future article, I'll try trimming down the single word features to only include significant words.

17May/1033

Text Classification for Sentiment Analysis – Precision and Recall

Accuracy is not the only metric for evaluating the effectiveness of a classifier. Two other useful metrics are precision and recall. These two metrics can provide much greater insight into the performance characteristics of a binary classifier.

Classifier Precision

Precision measures the exactness of a classifier. A higher precision means less false positives, while a lower precision means more false positives. This is often at odds with recall, as an easy way to improve precision is to decrease recall.

Classifier Recall

Recall measures the completeness, or sensitivity, of a classifier. Higher recall means less false negatives, while lower recall means more false negatives. Improving recall can often decrease precision because it gets increasingly harder to be precise as the sample space increases.

F-measure Metric

Precision and recall can be combined to produce a single metric known as F-measure, which is the weighted harmonic mean of precision and recall. I find F-measure to be about as useful as accuracy. Or in other words, compared to precision & recall, F-measure is mostly useless, as you'll see below.

Measuring Precision and Recall of a Naive Bayes Classifier

The NLTK metrics module provides functions for calculating all three metrics mentioned above. But to do so, you need to build 2 sets for each classification label: a reference set of correct values, and a test set of observed values. Below is a modified version of the code from the previous article, where we trained a Naive Bayes Classifier. This time, instead of measuring accuracy, we'll collect reference values and observed values for each label (pos or neg), then use those sets to calculate the precision, recall, and F-measure of the naive bayes classifier. The actual values collected are simply the index of each featureset using enumerate.

import collections
import nltk.metrics
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews

def word_feats(words):
	return dict([(word, True) for word in words])

negids = movie_reviews.fileids('neg')
posids = movie_reviews.fileids('pos')

negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids]

negcutoff = len(negfeats)*3/4
poscutoff = len(posfeats)*3/4

trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print 'train on %d instances, test on %d instances' % (len(trainfeats), len(testfeats))

classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)

for i, (feats, label) in enumerate(testfeats):
	refsets[label].add(i)
	observed = classifier.classify(feats)
	testsets[observed].add(i)

print 'pos precision:', nltk.metrics.precision(refsets['pos'], testsets['pos'])
print 'pos recall:', nltk.metrics.recall(refsets['pos'], testsets['pos'])
print 'pos F-measure:', nltk.metrics.f_measure(refsets['pos'], testsets['pos'])
print 'neg precision:', nltk.metrics.precision(refsets['neg'], testsets['neg'])
print 'neg recall:', nltk.metrics.recall(refsets['neg'], testsets['neg'])
print 'neg F-measure:', nltk.metrics.f_measure(refsets['neg'], testsets['neg'])

Precision and Recall for Positive and Negative Reviews

I found the results quite interesting:

pos precision: 0.651595744681
pos recall: 0.98
pos F-measure: 0.782747603834
neg precision: 0.959677419355
neg recall: 0.476
neg F-measure: 0.636363636364

So what does this mean?

  1. Nearly every file that is pos is correctly identified as such, with 98% recall. This means very few false negatives in the pos class.
  2. But, a file given a pos classification is only 65% likely to be correct. Not so good precision leads to 35% false positives for the pos label.
  3. Any file that is identified as neg is 96% likely to be correct (high precision). This means very few false positives for the neg class.
  4. But many files that are neg are incorrectly classified. Low recall causes 52% false negatives for the neg label.
  5. F-measure provides no useful information. There's no insight to be gained from having it, and we wouldn't lose any knowledge if it was taken away.

Improving Results with Better Feature Selection

One possible explanation for the above results is that people use normally positives words in negative reviews, but the word is preceded by "not" (or some other negative word), such as "not great". And since the classifier uses the bag of words model, which assumes every word is independent, it cannot learn that "not great" is a negative. If this is the case, then these metrics should improve if we also train on multiple words, a topic I'll explore in a future article.

Another possibility is the abundance of naturally neutral words, the kind of words that are devoid of sentiment. But the classifier treats all words the same, and has to assign each word to either pos or neg. So maybe otherwise neutral or meaningless words are being placed in the pos class because the classifier doesn't know what else to do. If this is the case, then the metrics should improve if we eliminate the neutral or meaningless words from the featuresets, and only classify using sentiment rich words. This is usually done using the concept of information gain, aka mutual information, to improve feature selection, which I'll also explore in a future article.

If you have your own theories to explain the results, or ideas on how to improve precision and recall, please share in the comments.

15Mar/105

NLTK Classifier Based Chunker Accuracy

The NLTK Book has been updated with an explanation of how to train a classifier based chunker, and I wanted to compare it's accuracy versus my previous tagger based chunker.

Tag Chunker

I already covered how to train a tagger based chunker, with the the discovery that a Unigram-Bigram TagChunker is the narrow favorite. I'll use this Unigram-Bigram Chunker as a baseline for comparison below.

Classifier Chunker

A Classifier based Chunker uses a classifier such as the MaxentClassifier to determine which IOB chunk tags to use. It's very similar to the TagChunker in that the Chunker class is really a wrapper around a Classifier based part-of-speech tagger. And both are trainable alternatives to a regular expression parser. So first we need to create a ClassifierTagger, and then we can wrap it with a ClassifierChunker.

Classifier Tagger

The ClassifierTagger below is an abstracted version of what's described in the Information Extraction chapter of the NLTK Book. It should theoretically work with any feature extractor and classifier class when created with the train classmethod. The kwargs are passed to the classifier constructor.

from nltk.tag import TaggerI, untag

class ClassifierTagger(TaggerI):
	'''Abstracted from "Training Classifier-Based Chunkers" section of

http://nltk.googlecode.com/svn/trunk/doc/book/ch07.html

	'''
	def __init__(self, feature_extractor, classifier):
		self.feature_extractor = feature_extractor
		self.classifier = classifier

	def tag(self, sent):
		history = []

		for i, word in enumerate(sent):
			featureset = self.feature_extractor(sent, i, history)
			tag = self.classifier.classify(featureset)
			history.append(tag)

		return zip(sent, history)

	@classmethod
	def train(cls, train_sents, feature_extractor, classifier_cls, **kwargs):
		train_set = []

		for tagged_sent in train_sents:
			untagged_sent = untag(tagged_sent)
			history = []

			for i, (word, tag) in enumerate(tagged_sent):
				featureset = feature_extractor(untagged_sent, i, history)
				train_set.append((featureset, tag))
				history.append(tag)

		classifier = classifier_cls.train(train_set, **kwargs)
		return cls(feature_extractor, classifier)

Classifier Chunker

The ClassifierChunker is a thin wrapper around the ClassifierTagger that converts between tagged tuples and parse trees. args and kwargs in __init__ are passed in to ClassifierTagger.train().

from nltk.chunk import ChunkParserI, tree2conlltags, conlltags2tree

class ClassifierChunker(nltk.chunk.ChunkParserI):
	def __init__(self, train_sents, *args, **kwargs):
		tag_sents = [tree2conlltags(sent) for sent in train_sents]
		train_chunks = [[((w,t),c) for (w,t,c) in sent] for sent in tag_sents]
		self.tagger = ClassifierTagger.train(train_chunks, *args, **kwargs)

	def parse(self, tagged_sent):
		if not tagged_sent: return None
		chunks = self.tagger.tag(tagged_sent)
		return conlltags2tree([(w,t,c) for ((w,t),c) in chunks])

Feature Extractors

Classifiers work on featuresets, which are created with feature extraction functions. Below are the feature extractors I evaluated, partly copied from the NLTK Book.

def pos(sent, i, history):
	word, pos = sent[i]
	return {'pos': pos}

def pos_word(sent, i, history):
	word, pos = sent[i]
	return {'pos': pos, 'word': word}

def prev_pos(sent, i, history):
	word, pos = sent[i]

	if i == 0:
		prevword, prevpos = '<START>', '<START>'
	else:
		prevword, prevpos = sent[i-1]

	return {'pos': pos, 'prevpos': prevpos}

def prev_pos_word(sent, i, history):
	word, pos = sent[i]

	if i == 0:
		prevword, prevpos = '<START>', '<START>'
	else:
		prevword, prevpos = sent[i-1]

	return {'pos': pos, 'prevpos': prevpos, 'word': word}

def next_pos(sent, i, history):
	word, pos = sent[i]

	if i == len(sent) - 1:
		nextword, nextpos = '<END>', '<END>'
	else:
		nextword, nextpos = sent[i+1]

	return {'pos': pos, 'nextpos': nextpos}

def next_pos_word(sent, i, history):
	word, pos = sent[i]

	if i == len(sent) - 1:
		nextword, nextpos = '<END>', '<END>'
	else:
		nextword, nextpos = sent[i+1]

	return {'pos': pos, 'nextpos': nextpos, 'word': word}

def prev_next_pos(sent, i, history):
	word, pos = sent[i]

	if i == 0:
		prevword, prevpos = '<START>', '<START>'
	else:
		prevword, prevpos = sent[i-1]

	if i == len(sent) - 1:
		nextword, nextpos = '<END>', '<END>'
	else:
		nextword, nextpos = sent[i+1]

	return {'pos': pos, 'nextpos': nextpos, 'prevpos': prevpos}

def prev_next_pos_word(sent, i, history):
	word, pos = sent[i]

	if i == 0:
		prevword, prevpos = '<START>', '<START>'
	else:
		prevword, prevpos = sent[i-1]

	if i == len(sent) - 1:
		nextword, nextpos = '<END>', '<END>'
	else:
		nextword, nextpos = sent[i+1]

	return {'pos': pos, 'nextpos': nextpos, 'word': word, 'prevpos': prevpos}

Training

Now that we have all the pieces, we can put them together with training.

NOTE: training the classifier takes a long time. If you want to reduce the time, you can increase min_lldelta or decrease max_iter, but you risk reducing the accuracy. Also note that the MaxentClassifier will sometimes produce nan for the log likelihood (I'm guessing this is a divide-by-zero error somewhere). If you hit Ctrl-C once at this point, you can stop the training and continue.

from nltk.corpus import conll2000
from nltk.classify import MaxentClassifier

train_sents = conll2000.chunked_sents('train.txt')
# featx is one of the feature extractors defined above
chunker = ClassifierChunker(train_sents, featx, MaxentClassifier,
	min_lldelta=0.01, max_iter=10)

Accuracy

I ran the above training code for each feature extractor defined above, and generated the charts below. ub still refers to the TagChunker, which is included to provide a comparison baseline. All the other labels on the X-Axis refer to a classifier trained with one of the above feature extraction functions, using the first letter of each part of the name (p refers to pos(), pnpw refers to prev_next_pos_word(), etc).

conll2000 chunk training accuracy
treebank chunk training accuracy

One of the most interesting results of this test is how including the word in the featureset affects the accuracy. The only time including the word improves the accuracy is if the previous part-of-speech tag is also included in the featureset. Otherwise, including the word decreases accuracy. And looking ahead with next_pos() and next_pos_word() produces the worst results of all, until the previous part-of-speech tag is included. So whatever else you have in a featureset, the most important features are the current & previous pos tags, which, not surprisingly, is exactly what the TagChunker trains on.

Custom Training Data

Not only can the ClassifierChunker be significantly more accurate than the TagChunker, it is also superior for custom training data. For my own custom chunk corpus, I was unable to get above 94% accuracy with the TagChunker. That may seem pretty good, but it means the chunker is unable to parse over 1000 known chunks! However, after training the ClassifierChunker with the prev_next_pos_word feature extractor, I was able to get 100% parsing accuracy on my own chunk corpus. This is a huge win, and means that the behavior of the ClassifierChunker is much more controllable thru manualation.

   
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