Intelligent classification of text-based content

A computing device comprising:

• memory;
• one or more hardware processors coupled to the memory;
• and
• one or more non-transitory computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:
• receiving text-based content comprising a plurality of characters;
• generating a plurality of character category sequences for the text-based content using the plurality of characters, wherein each character category sequence, among the plurality of character category sequences, comprises multiple character category identifiers for multiple characters, respectively, of a different character sequence in the text-based content, and wherein each character category identifier, among the multiple character category identifiers, identifies one of a plurality of predefined character categories;
• calculating a frequency distribution of the plurality of character category sequences, wherein the frequency distribution represents relative frequencies of occurrence of unique character category sequences among a total number of the plurality of character category sequences;
• and
• classifying, using a machine learning model, the text-based content into one of multiple classes based on the calculated frequency distribution of the plurality of character category sequences, wherein a total number of the plurality of predefined character categories is smaller than a total number of possible unique characters such that dimension of input to the machine learning model is reduced as compared with input based on a frequency distribution of character sequences for the text-based content.

A computer-implemented method of training a machine learning model for improved classification of text-based content, the method comprising:

• receiving a plurality of examples of text-based content, each of which has a known classification;
• and
• for each of the plurality of examples of text-based content:
• generating a plurality of character category sequences for the example of text-based content using a plurality of characters of the example of text-based content, wherein each character category sequence, among the plurality of character category sequences for the example of text-based content, comprises multiple character category identifiers for multiple characters, respectively, of a different character sequence in the example of text-based content, and wherein each character category identifier, among the multiple character category identifiers, identifies one of a plurality of predefined character categories;
• calculating a frequency distribution of the plurality of character category sequences for the example of text-based content, wherein the frequency distribution represents relative frequencies of occurrence of unique character category sequences, for the example of text-based content, among a total number of the plurality of character category sequences for the example of text-based content;
• and
• training a machine learning model based on the calculated frequency distribution of the plurality of character category sequences for the example of text-based content and the known classification of the example of text-based content, wherein a total number of the plurality of predefined character categories is smaller than a total number of possible unique characters such that dimension of input to the machine learning model is reduced as compared with input based on a frequency distribution of character sequences for the example of text-based content.