Text data representation learning using random document embedding

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United States of America Patent

PATENT NO 11823013
APP PUB NO 20190065986A1
SERIAL NO

15689799

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Abstract

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Embodiments of the present invention provide a computer-implemented method for performing unsupervised feature representation learning for text data. The method generates reference text data having a set of random text sequences, in which each text sequence of set of random text sequences is of a random length and comprises a number of random words, and in which each random length is sampled from a minimum length to a maximum length. The random words of each text sequence in the set are drawn from a distribution. The method generates a feature matrix for raw text data based at least in part on a set of computed distances between the set of random text sequences and the raw text data. The method provides the feature matrix as an input to one or more machine learning models.

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Patent Owner(s)

  • IBM CORPORATION

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Witbrock, Michael J Ossining, US 8 344
Wu, Lingfei Croton on Hudson, US 45 126

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