Global confidence classifier for information retrieval in contact centers

A method of leveraging a global confidence classifier for information retrieval of knowledge-only bots in contact centers, the method comprising:

• receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system;
• performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query;
• identifying, by the computing system, a subset of documents most likely to be responsive to the user query using a first classifier machine learning model;
• and
• re-ranking, by the computing system using a global confidence classifier machine learning model, the subset of documents identified to be most likely to be responsive to the user query based on the first classifier machine learning model;
• wherein the global confidence classifier machine learning model is trained based on a confidence classifier that uses at least five input features;
• and
• wherein the at least five input features comprise a first alignment score between the user query and a corresponding document, a second alignment score between the corresponding document and the user query, a cosine similarity score between the user query and the corresponding document, a document score from a document classifier, and a word overlap score that calculates a number of n-grams that are similar between the user query and the corresponding document.

A system for leveraging a global confidence classifier for information retrieval of knowledge-only bots in contact centers, the system comprising:

• at least one processor;
• and
• at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to:
• receive a user query from a contact center client communicating with a knowledge-only bot of the system;
• perform feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query;
• identify, using a document classifier machine learning model, a subset of documents most likely to be responsive to the user query;
• re-rank, using a global confidence classifier machine learning model, the subset of documents identified to be most likely to be responsive to the user query based on the feature extraction and the document classifier machine learning model;
• wherein the global confidence classifier machine learning model is trained based on a confidence classifier that uses at least five input features;
• and
• wherein the at least five input features comprise a first alignment score between the user query and a corresponding document, a second alignment score between the corresponding document and the user query, a cosine similarity score between the user query and the corresponding document, a document score from a document classifier, and a word overlap score that calculates a number of n-grams that are similar between the user query and the corresponding document.