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        "author": "Georges Alkhouri",
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        "description": "l3wtransformer\n==============\n\n> A word hashing method to reduce the dimensionality of the bag-of-words term vectors. It is based on letter n-gram. Given a word (e.g. good), it first adds word starting and ending marks to the word (e.g. #good#). Then, breaks the word into letter n-grams (e.g. letter trigrams: #go, goo, ood, od#). Finally, the word is represented using a vector of letter n-grams.\n\n[Huang et al.2013, Learning Deep Structured Semantic Models for Web Search using Clickthrough Data]\n\n---\n\nThis implementation supports the transformation from **text into sequences of numbers**, with the numbers indicating the descending word frequency.\n\nFor example:\n\n*Lorem ipsum dolor sit amet, consectetuer adipiscing elit...* is transformed into *23, 1, 80, 86, 47, 50001, 21, 59, 83, 93, 14, 50003, 4, 7*\n\nAlso, after each word flags indicating lower case, upper case, mixed case or initial capitalization are added.\n\n### To do\n\nThere will be an implementation supporting the transformation from **text into bag-of-word vectors**.\n\nInstall\n-------\n\n```\npip install l3wtransformer\n```\n\nUsage\n-----\n\n```\nfrom l3wtransformer import L3wTransformer\n\nl3wt = L3wTransformer()\n\nl3wt.fit_on_texts(['First example.', 'And one more!'])\nl3wt.texts_to_sequences(['One example', '2nd exa.'])\n\n# [[5, 18, 17, 50001, 2, 10, 24, 6, 15, 20, 50003], [16, 50003, 2, 10, 50003]]\n```\n\n\n",
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        "keywords": "machine-learning natural-language-processing preprocessing",
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        "summary": "A word hashing method based on vectors of letter n-grams. Currently transforms text into sequences of numbers.",
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