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I am trying to use NLTK for training a Naive Bayes classifier for multi-class text classification. But I do not have access to the original texts. I am provided with is a file in SVM Light format (one instance each line with feature:value pair). I simply have to import this file and train and test Naive Bayes classifier using this dataset. I was wondering if there is some way to import this file into NLTK and use it directly for training classifiers.

ritesh
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1 Answers1

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According to nltk's own documentation this is achieved something like this:

Excerpt from Documentation:

scikit-learn (http://scikit-learn.org) is a machine learning library for Python. It supports many classification algorithms, including SVMs, Naive Bayes, logistic regression (MaxEnt) and decision trees.

This package implement a wrapper around scikit-learn classifiers. To use this wrapper, construct a scikit-learn estimator object, then use that to construct a SklearnClassifier. E.g., to wrap a linear SVM with default settings:

Example:

>>> from sklearn.svm import LinearSVC
>>> from nltk.classify.scikitlearn import SklearnClassifier
>>> classif = SklearnClassifier(LinearSVC())

See: http://www.nltk.org/api/nltk.classify.html#module-nltk.classify.scikitlearn

alvas
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James Mills
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  • do note that you're using `sklearn` to convert the corpus into svm format and use nltk's wrapper to `sklearn` to call the classifier. – alvas Mar 24 '14 at 06:43