Device and method for natural language processing through statistical model comparison

A system for natural language processing for a human-machine interface through statistical model comparison comprising one or more processors configured to:

• receive a first set of words and a second set of words;
• calculate a first likelihood-based measure representing how well a first model can be fit to the first and second sets of words, the first model comprising a shared parametric distribution representing both the first and second sets of words;
• calculate a second likelihood-based measure representing how well a second model can be fit to the first and second sets of words, the second model comprising a first parametric distribution representing the first set of words and a second parametric distribution representing the second set of words;
• calculate a similarity score based on a ratio of the first likelihood measure to the second likelihood measure, the similarity score being representative of the similarity between the first and second sets of words;
• output the similarity score to the human-machine interface to determine a semantic similarity between the first set of words and the second set of words;
• and
• associate, through the human-machine interface, the first set of words with the second set of words based on the semantic similarity to determine a response.

A non-transient computer readable medium containing programming instructions for natural language processing for a human-machine interface through statistical model comparison that, when executed by a computer, cause the computer to:

• receive a first set of words and a second set of words;
• calculate a first likelihood-based measure representing how well a first model can be fit to the first and second sets of words, the first model comprising a shared parametric distribution representing both the first and second sets of words;
• calculate a second likelihood-based measure representing how well a second model can be fit to the first and second sets of words, the second model comprising a first parametric distribution representing the first set of words and a second parametric distribution representing the second set of words;
• calculate a similarity score based on a ratio of the first likelihood measure to the second likelihood measure, the similarity score being representative of the similarity between the first and second sets of words;
• output the similarity score to the human-machine interface to determine a semantic similarity between the first set of words and the second set of words;
• and
• associate, through the human-machine interface, the first set of words with the second set of words based on the semantic similarity to determine a response.