hext: a text classification library

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Please see README.md

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Change logNone available
Dependenciesbase (>=4.7 && <5), containers, hext, text, unordered-containers [details]
Copyright2016 David Anekstein
AuthorDavid Anekstein
CategoryNatural Language Processing
Home pagehttps://github.com/aneksteind/hext#readme
Source repositoryhead: git clone https://github.com/aneksteind/hext
UploadedThu Jul 28 19:39:14 UTC 2016 by aneksteind




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Readme for hext-

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This is currently the beginning of a text classification library.

##Installation/Running stack install hext

hackage - https://hackage.haskell.org/package/hext-

To run:
stack build
stack exec hext-exe


Currently, the only algorithm implementation is the Naive Bayes algorithm: to run your own data through this algorithm in order to classify your text, you need:

In order to run the program, the classified data specified above must be converted into a BayesModel a using the teach function, where a is your own defined data type representing the class to classify your text. Your class must be and instance of Ord and Eq.

With your new BayesModel filled with knowledge, it's time to classify your text using runBayes. An example of this can be seen in app/Main.hs where data Class = Positive | Negative deriving (Eq, Ord, Show) to label movie reviews as either positive or negative.


I encourage contributing to this package, in the form of implementing algorithms that are not yet in the project, improving efficiency, or the like.