Classes and interfaces for labeling tokens with category labels (or “class labels”). Typically, labels are represented with strings (such as 'health' or 'sports'). Classifiers can be used to perform a wide range of classification tasks. For example, classifiers can be used...

  • to classify documents by topic
  • to classify ambiguous words by which word sense is intended
  • to classify acoustic signals by which phoneme they represent
  • to classify sentences by their author


In order to decide which category label is appropriate for a given token, classifiers examine one or more ‘features’ of the token. These “features” are typically chosen by hand, and indicate which aspects of the token are relevant to the classification decision. For example, a document classifier might use a separate feature for each word, recording how often that word occurred in the document.


The features describing a token are encoded using a “featureset”, which is a dictionary that maps from “feature names” to “feature values”. Feature names are unique strings that indicate what aspect of the token is encoded by the feature. Examples include 'prevword', for a feature whose value is the previous word; and 'contains-word(library)' for a feature that is true when a document contains the word 'library'. Feature values are typically booleans, numbers, or strings, depending on which feature they describe.

Featuresets are typically constructed using a “feature detector” (also known as a “feature extractor”). A feature detector is a function that takes a token (and sometimes information about its context) as its input, and returns a featureset describing that token. For example, the following feature detector converts a document (stored as a list of words) to a featureset describing the set of words included in the document:

>>> # Define a feature detector function.
>>> def document_features(document):
...     return dict([('contains-word(%s)' % w, True) for w in document])

Feature detectors are typically applied to each token before it is fed to the classifier:

>>> # Classify each Gutenberg document.
>>> from nltk.corpus import gutenberg
>>> for fileid in gutenberg.fileids(): 
...     doc = gutenberg.words(fileid) 
...     print fileid, classifier.classify(document_features(doc)) 

The parameters that a feature detector expects will vary, depending on the task and the needs of the feature detector. For example, a feature detector for word sense disambiguation (WSD) might take as its input a sentence, and the index of a word that should be classified, and return a featureset for that word. The following feature detector for WSD includes features describing the left and right contexts of the target word:

>>> def wsd_features(sentence, index):
...     featureset = {}
...     for i in range(max(0, index-3), index):
...         featureset['left-context(%s)' % sentence[i]] = True
...     for i in range(index, max(index+3, len(sentence))):
...         featureset['right-context(%s)' % sentence[i]] = True
...     return featureset

Training Classifiers

Most classifiers are built by training them on a list of hand-labeled examples, known as the “training set”. Training sets are represented as lists of (featuredict, label) tuples.


accuracy(classifier, gold)
apply_features(feature_func, toks[, labeled]) Use the LazyMap class to construct a lazy list-like object that is analogous to map(feature_func, toks).
call_megam(args) Call the megam binary with the given arguments.
config_megam([bin]) Configure NLTK’s interface to the megam maxent optimization package.
log_likelihood(classifier, gold)
rte_classifier(trainer[, features]) Classify RTEPairs


BinaryMaxentFeatureEncoding(labels, mapping) A feature encoding that generates vectors containing a binary
ClassifierI A processing interface for labeling tokens with a single category label (or “class”).
ConditionalExponentialClassifier Alias for MaxentClassifier.
DecisionTreeClassifier(label[, ...])
MaxentClassifier(encoding, weights[, ...]) A maximum entropy classifier (also known as a “conditional exponential classifier”).
MultiClassifierI A processing interface for labeling tokens with zero or more category labels (or “labels”).
NaiveBayesClassifier(label_probdist, ...) A Naive Bayes classifier.
PositiveNaiveBayesClassifier(label_probdist, ...)
RTEFeatureExtractor(rtepair[, stop, lemmatize]) This builds a bag of words for both the text and the hypothesis after throwing away some stopwords, then calculates overlap and difference.
Senna(senna_path, operations[, encoding])
SklearnClassifier(estimator[, dtype, sparse]) Wrapper for scikit-learn classifiers.
TypedMaxentFeatureEncoding(labels, mapping) A feature encoding that generates vectors containing integer,
WekaClassifier(formatter, model_filename)