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TEXT CLASSIFICATION BY RANKING WITH CONVOLUTIONAL NEURAL NETWORKS

  • US 20170308790A1
  • Filed: 04/21/2016
  • Published: 10/26/2017
  • Est. Priority Date: 04/21/2016
  • Status: Abandoned Application
First Claim
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1. A method comprising:

  • configuring a convolutional neural network (CNN) for classifying text based on word embedding features into a predefined set of classes identified by class labels, the predefined set of classes including a class that is labeled none-of-the-above for text that does not fit into any of the other classes in the predefined set of classes, the configuring comprising;

    receiving a set of training data that includes for each training round;

    training text, a correct class label that correctly classifies the training text, and an incorrect class label that incorrectly classifies the training text, the correct class label and the incorrect class label selected from the class labels that identify the predefined set of classes;

    training the CNN based on the set of training data, the training including;

    learning parameters of class distributed vector representations (DVRs) of each of the predefined set of classes, the learning including minimizing a pair-wise ranking loss function over the set of training data and causing the CNN to generate;

    a score of less than zero in response to a correct class label of none-of-the-above, and a score of greater than zero in response to a correct class label having any other value; and

    a score of less than zero in response to an incorrect class label; and

    generating a class embedding matrix of the class DVRs of the predefined set of classes that excludes a class embedding for the none-of-the-above class, each column in the class embedding matrix corresponding to one of the predefined classes.

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