datagram is JS classification library it can automatically classify and recognize text documents using n-grams method.
That is sometime useful in Computational linguistics as-well as in other scientific fields.
Its was primarily developed for language guessing but not limited to it.
Algorithm is based on the classification technique described in Cavnar & Trenkle, "N-Gram-Based Text Categorization". (https://www.let.rug.nl/vannoord/TextCat/textcat.pdf)
creating your own datasets can be useful for categorizing text into topics. Data should be at much of data as you can get in order to get useful datasets.
Just set in options:
DEFAULT_MODE: 'profilize'
and see "../samples/profilize/index.json" for example
var options = {
DEFAULT_DATASETS: ['English', 'German', 'French', 'Polish', 'Italian', 'Croatian'],
DEFAULT_DATASETS_DIR: './datagram/LM/',
DEFAULT_DATASETS_EXT: '.lm',
DEFAULT_INPUT: 'string', // string, file
DEFAULT_MODE: 'profilize', // classify, profilize
debug: false,
callback: function (error, result) {
return new datagramProcessed(error, result);
},
onDrain: function () {
return new datagramDone();
},
onDebug: function (data, addon) {
return new datagramDebug(data, addon);
}
};
var dg = new datagram(options);
// To make Dataset Profiles
//tc.queue(../samples/profilize/index.json);If you are having trouble, have found a bug, or want to contribute don't be shy.
Open a ticket on GitHub.
datagram source-code uses the The MIT License (MIT), see our LICENSE file.
The MIT License (MIT)
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