Device and method for natural language processing through statistical model comparison
A computer-implemented method comprising: receiving the first set of words and the second set of words, wherein each of the first and second sets of words; calculating 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; calculating 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; calculating 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; and outputting the similarity score.
1. A computer-implemented method for natural language processing for a human-machine interface through statistical model comparison, comprising:
receiving a first set of words and a second set of words;
calculating 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;
calculating 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;
calculating 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;
outputting 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
associating, 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.
2. The method of claim 1 wherein the first, second and shared parametric distributions are first, second and shared likelihood functions respectively, each defined by a respective set of parameters.
3. The method of claim 2 wherein the first and second sets of words are first and second sets of word embeddings constrained to lie on the surface of a unit hypersphere, and wherein the first, second and shared likelihood functions are in accordance with the von Mises-Fisher distribution.
4. The method of claim 2 further comprising determining maximum likelihood estimates for the parameters for each likelihood function.
5. The method of claim 4 wherein the first likelihood-based measure is calculated based on a maximum value for the shared likelihood function and the second likelihood-based measure is calculated based on maximum values for the first and second likelihood functions.
6. The method of claim 5 wherein the first and second information criteria are in accordance with the Takeuchi Information Criterion or the Akaiki Information Criterion.
7. The method of claim 1 wherein the first and second likelihood based measures are first and second information criteria respectively.
8. The method of claim 1 wherein the first and second likelihood-based measures each comprise a respective penalty based on complexity for the respective model.
9. 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.
10. The system of claim 9 wherein the first, second and shared parametric distributions are first, second and shared likelihood functions respectively, each defined by a respective set of parameters.
11. The system of claim 10 wherein the first and second sets of words are first and second sets of word embeddings constrained to lie on the surface of a unit hypersphere, and wherein the first, second and shared likelihood functions are in accordance with the von Mises-Fisher distribution.
12. The system of claim 10 wherein the one or more processors are further configured to determine maximum likelihood estimates for the parameters for each likelihood function.
13. The system of claim 12 wherein the first likelihood-based measure is calculated based on a maximum value for the shared likelihood function and the second likelihood-based measure is calculated based on respective maximum values for the first and second likelihood functions.
14. The system of claim 9 wherein the first and second likelihood based measures are first and second information criteria respectively.
15. The system of claim 14 wherein the first and second information criteria are in accordance with the Takeuchi Information Criterion or the Akaiki Information Criterion.
16. The system of claim 9 wherein the first and second likelihood-based measures each comprise a respective penalty based on complexity for the respective model.
17. 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.