Real time learning of text classification models for fast and efficient labeling of training data and customization

A computer-implemented method of training a plurality of text classification models, comprising:

• predefining multiple labels corresponding to various classes of input text samples for an initial dataset of the input text samples;
• manually assigning the predefined labels to selected ones of the initial dataset of the input text samples to indicate that the input text samples belong in particular ones of the various classes;
• once a predefined label has been manually assigned to each of the initial dataset of the input text samples, providing the initial dataset of the input text samples as training data to train a plurality of machine learning classifiers for generating the plurality of text classification models, respectively, the plurality of text classification models corresponding to the various classes, respectively;
• generating the plurality of text classification models taking into account a covariance of attributes that can be expressed as scalars or Boolean dimensions, the attributes including one or more of individual keywords, weighted keywords, and dimensions of a vectorized keyword in the initial dataset of the input text samples;
• while the plurality of machine learning classifiers train with the training data, providing label suggestions in real-time by the plurality of text classification models regarding what predefined labels to assign to any of the input text samples in the training data;
• manually assigning the predefined labels to at least some of the input text samples in accordance with the label suggestions from the plurality of text classification models;
• and
• providing the at least some of the input text samples as supplements to the training data for the plurality of machine learning classifiers.

A system for training a plurality of text classification models, comprising:

• a server including at least one processor operative to execute at least one program out of at least one memory:
• to receive an initial dataset of input text samples with predefined labels manually assigned thereto to indicate that the input text samples belong in particular ones of various classes;
• to provide the initial dataset of the input text samples as training data to train a plurality of machine learning classifiers for generating the plurality of text classification models, respectively, the plurality of text classification models corresponding to the various classes, respectively;
• to generate the plurality of text classification models taking into account individual keywords or pairs of keywords that occur in a predetermined threshold number of positive examples of the input text samples for a particular class, but do not occur in more than a specified number of negative examples of the input text samples for the particular class;
• while the plurality of machine learning classifiers train with the training data, to provide, through the plurality of text classification models, label suggestions in real-time regarding what predefined labels to assign to any of the input text samples in the training data;
• to receive at least some of the input text samples with the predefined labels manually assigned thereto in accordance with the label suggestions;
• and
• to provide the at least some of the input text samples as supplements to the training data for the plurality of machine learning classifiers.

A method comprising:

• receiving an initial dataset of input text samples with predefined labels manually assigned thereto to indicate that the input text samples belong in particular ones of various classes;
• providing the initial dataset of the input text samples as training data to train a plurality of machine learning classifiers for generating a plurality of text classification models, respectively, the plurality of text classification models corresponding to the various classes, respectively;
• while the plurality of machine learning classifiers train with the training data, providing, through the plurality of text classification models, label suggestions in real-time regarding what predefined labels to assign to any of the input text samples in the training data;
• receiving at least some of the input text samples with the predefined labels manually assigned thereto in accordance with the label suggestions;
• and
• providing the at least some of the input text samples as supplements to the training data for the plurality of machine learning classifiers.