99 lines
4.4 KiB
Python
99 lines
4.4 KiB
Python
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# Natural Language Toolkit: Classifiers
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Classes and interfaces for labeling tokens with category labels (or
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"class labels"). Typically, labels are represented with strings
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(such as ``'health'`` or ``'sports'``). Classifiers can be used to
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perform a wide range of classification tasks. For example,
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classifiers can be used...
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- to classify documents by topic
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- to classify ambiguous words by which word sense is intended
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- to classify acoustic signals by which phoneme they represent
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- to classify sentences by their author
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Features
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========
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In order to decide which category label is appropriate for a given
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token, classifiers examine one or more 'features' of the token. These
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"features" are typically chosen by hand, and indicate which aspects
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of the token are relevant to the classification decision. For
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example, a document classifier might use a separate feature for each
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word, recording how often that word occurred in the document.
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Featuresets
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===========
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The features describing a token are encoded using a "featureset",
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which is a dictionary that maps from "feature names" to "feature
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values". Feature names are unique strings that indicate what aspect
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of the token is encoded by the feature. Examples include
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``'prevword'``, for a feature whose value is the previous word; and
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``'contains-word(library)'`` for a feature that is true when a document
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contains the word ``'library'``. Feature values are typically
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booleans, numbers, or strings, depending on which feature they
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describe.
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Featuresets are typically constructed using a "feature detector"
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(also known as a "feature extractor"). A feature detector is a
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function that takes a token (and sometimes information about its
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context) as its input, and returns a featureset describing that token.
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For example, the following feature detector converts a document
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(stored as a list of words) to a featureset describing the set of
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words included in the document:
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>>> # Define a feature detector function.
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>>> def document_features(document):
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... return dict([('contains-word(%s)' % w, True) for w in document])
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Feature detectors are typically applied to each token before it is fed
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to the classifier:
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>>> # Classify each Gutenberg document.
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>>> from nltk.corpus import gutenberg
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>>> for fileid in gutenberg.fileids(): # doctest: +SKIP
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... doc = gutenberg.words(fileid) # doctest: +SKIP
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... print fileid, classifier.classify(document_features(doc)) # doctest: +SKIP
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The parameters that a feature detector expects will vary, depending on
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the task and the needs of the feature detector. For example, a
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feature detector for word sense disambiguation (WSD) might take as its
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input a sentence, and the index of a word that should be classified,
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and return a featureset for that word. The following feature detector
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for WSD includes features describing the left and right contexts of
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the target word:
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>>> def wsd_features(sentence, index):
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... featureset = {}
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... for i in range(max(0, index-3), index):
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... featureset['left-context(%s)' % sentence[i]] = True
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... for i in range(index, max(index+3, len(sentence))):
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... featureset['right-context(%s)' % sentence[i]] = True
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... return featureset
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Training Classifiers
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====================
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Most classifiers are built by training them on a list of hand-labeled
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examples, known as the "training set". Training sets are represented
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as lists of ``(featuredict, label)`` tuples.
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"""
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from nltk.classify.api import ClassifierI, MultiClassifierI
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from nltk.classify.megam import config_megam, call_megam
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from nltk.classify.weka import WekaClassifier, config_weka
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from nltk.classify.naivebayes import NaiveBayesClassifier
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from nltk.classify.positivenaivebayes import PositiveNaiveBayesClassifier
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from nltk.classify.decisiontree import DecisionTreeClassifier
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from nltk.classify.rte_classify import rte_classifier, rte_features, RTEFeatureExtractor
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from nltk.classify.util import accuracy, apply_features, log_likelihood
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from nltk.classify.scikitlearn import SklearnClassifier
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from nltk.classify.maxent import (MaxentClassifier, BinaryMaxentFeatureEncoding,
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TypedMaxentFeatureEncoding,
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ConditionalExponentialClassifier)
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from nltk.classify.senna import Senna
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from nltk.classify.textcat import TextCat
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