Machine Learning Based Intent Resolution for a Digital Assistant

A system comprising

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• determine, based on a received utterance, a first vector using a term frequency-inverse document frequency (TF-IDF);
• determine, based on the received utterance, a second vector using a Global Vectors (GloVe) model;
• generate a third vector by combining the first vector and the second vector;
• and
• use the third vector to classify, using a machine learning model, the utterance to determine an intent associated with the utterance.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• determining, based on a received utterance, a first vector using a term frequency-inverse document frequency (TF-IDF);
• determining, based on the received utterance, a second vector using a Global Vectors (GloVe) model;
• generating a third vector by combining the first vector and the second vector;
• and
• using the third vector to perform at least one of:
• classifying, using a machine learning model, the utterance to determine an intent associated with the utterance;
• or
• training the machine-learning model, wherein the machine learning model is used to determine the intent associated with the utterance,
• wherein the machine learning model comprises a support-vector machine classifier.