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1. WO2021046191 - CLASSIFYING BUSINESS SUMMARIES AGAINST A HIERARCHICAL INDUSTRY CLASSIFICATION STRUCTURE USING SUPERVISED MACHINE LEARNING

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A computing apparatus comprising:

one or more processors;

one or more memories; and

a word-based sub classifier configured to generate a first probability distribution for a text-based business summary across a plurality of industry classifications in a hierarchical industry classification structure, wherein:

the word-based sub classifier is trained on a training set comprising a plurality of text-based business summaries, wherein each text-based business summary, from the plurality of text-based business summaries, has a known correspondence to an industry classification, from the plurality of industry classifications, in the hierarchical industry classification structure, and

training of the word-based sub classifier is completed when an evaluation metric satisfies one or more early stopping criteria,

a category-based sub classifier configured to generate a second probability distribution for the text-based business summary across the plurality of industry classifications in the hierarchical industry classification structure,

a meta classifier configured to determine a predicted probability distribution for the text- based business summary across the plurality of industry classifications in the hierarchical industry classification structure based upon the first probability distribution generated by the word-based sub classifier and second probability distribution generated by the category -based sub classifier.

2. The computing apparatus as recited in Claim 1, wherein the word-based sub classifier is further configured to use a neural network to determine a vector representation for a particular text-based business summary from the plurality of text-based business summaries by:

determining a vector representation for each word in the particular text-based business summary, and

determining an average vector representation based upon the vector representations for each word in the particular text-based business summary.

3. The computing apparatus as recited in Claim 1, wherein training the word-based sub classifier includes updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function, where a loss in the loss function represents a difference between an estimated probability and a known probability that a particular text-based business summary, from the plurality of text-based business summaries in the training set, corresponds to a particular industry classification in the hierarchical industry classification structure.

4. The computing apparatus as recited in Claim 3, wherein updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function includes, for a particular industry classification in the hierarchical industry classification, increasing representation of a parent industry classification to the particular industry classification in the hierarchical industry classification.

5. The computing apparatus as recited in Claim 4, wherein the word-based sub classifier is further configured to revise a value that corresponds to the parent industry classification in a one-hot vector representation for the particular text-based validation summary.

6. The computing apparatus as recited in Claim 1, wherein:

the evaluation metric is an hFi score calculated based upon results of processing a set of validation summaries using the word-based sub classifier.

7. The computing apparatus as recited in Claim 1, wherein the predicted probability distribution for the text-based business summary across the plurality of industry classifications in the hierarchical industry classification structure is a geometric mean of the first probability distribution generated by the word-based sub classifier and the second probability distribution generated by the category-based sub classifier.

8. One or more non-transitory computer-readable media storing instructions which, when processed by one or more processors, cause:

a word-based sub classifier to generate a first probability distribution for a text-based business summary across a plurality of industry classifications in a hierarchical industry classification structure, wherein:

the word-based sub classifier is trained on a training set comprising a plurality of text-based business summaries, wherein each text-based business summary, from the plurality of text-based business summaries, has a known correspondence to an industry classification, from the plurality of industry classifications, in the hierarchical industry classification structure, and

training of the word-based sub classifier is completed when an evaluation metric satisfies one or more early stopping criteria,

a category -based sub classifier to generate a second probability distribution for the text- based business summary across the plurality of industry classifications in the hierarchical industry classification structure,

a meta classifier to determine a predicted probability distribution for the text-based business summary across the plurality of industry classifications in the hierarchical industry classification structure based upon the first probability distribution generated by the word-based sub classifier and second probability distribution generated by the category -based sub classifier.

9. The one or more non-transitory computer-readable media as recited in Claim 8, further comprising additional instructions which, when processed by the one or more processors, cause the word-based sub classifier to use a neural network to determine a vector representation for a particular text-based business summary from the plurality of text- based business summaries by:

determining a vector representation for each word in the particular text-based business summary, and

determining an average vector representation based upon the vector representations for each word in the particular text-based business summary.

10. The one or more non-transitory computer-readable media as recited in Claim 8, wherein training the word-based sub classifier includes updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function, where a loss in the loss function represents a difference between an estimated probability and a known probability that a particular text-based business sum man,', from the plurality of text-based business summaries in the training set, corresponds to a particular industry classification in the hierarchical industry classification structure.

11. The one or more non-transitory computer-readable media as recited in Claim 10, wherein updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function includes, for a particular industry classification in the hierarchical industry classification, increasing representation of a parent industry classification to the particular industry classification in the hierarchical industry classification.

12. The one or more non-transitory computer-readable media as recited in Claim 11, further comprising additional instructions which, when processed by the one or more processors, cause the word-based sub classifier to revise a value that corresponds to the parent industry classification in a one-hot vector representation for the particular text-based validation summary.

13. The one or more non-transitory computer-readable media as recited in Claim 8, wherein:

the evaluation metric is an hFi score calculated based upon results of processing a set of validation summaries using the word-based sub classifier.

14. The one or more non-transitory computer-readable media as recited in Claim 8, wherein the predicted probability distribution for the text-based business summary across the

plurality of industry classifications in the hierarchical industry classification structure is a geometric mean of the first probability distribution generated by the word-based sub classifier and the second probability distribution generated by the category-based sub classifier.

15. A computer-implemented method comprising:

a word-based sub classifier generating a first probability distribution for a text-based business summary across a plurality of industry classifications in a hierarchical industry classification structure, wherein:

the word-based sub classifier is trained on a training set comprising a plurality of text-based business summaries, wherein each text-based business summary, from the plurality of text-based business summaries, has a known correspondence to an industry classification, from the plurality of industry classifications, in the hierarchical industry classification structure, and

training of the word-based sub classifier is completed when an evaluation metric satisfies one or more early stopping criteria,

a category-based sub classifier generating a second probability distribution for the text- based business summary across the plurality of industry classifications in the hierarchical industry classification structure,

a meta classifier determining a predicted probability distribution for the text-based business summary across the plurality of industry classifications in the hierarchical industry classification structure based upon the first probability distribution generated by the word-based sub classifier and second probability distribution generated by the category -based sub classifier.

16. The computer-implemented method as recited in Claim 15, further comprising the word- based sub classifier using a neural network to determine a vector representation for a particular text-based business summary from the plurality of text-based business summaries by:

determining a vector representation for each word in the particular text-based business summary, and

determining an average vector representation based upon the vector representations for each word in the particular text-based business summary.

17. The computer-implemented method as recited in Claim 15, wherein training the word- based sub classifier includes updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function, where a loss in the loss function represents a difference between an estimated probability and a known probability that a particular text-based business summary, from the plurality of text-based business summaries in the training set, corresponds to a particular industry classification in the hierarchical industry classification structure.

18. The computer-implemented method as recited in Claim 17, wherein updating weight values in a hidden layer of a neural network used by the word-based sub classifier to minimize a loss function includes, for a particular industry classification in the hierarchical industry classification, increasing representation of a parent industry classification to the particular industry classification in the hierarchical industry classification.

19. The computer-implemented method as recited in Claim 18, further comprising the word- based sub classifier revising a value that corresponds to the parent industry classification in a one-hot vector representation for the particular text-based validation summary.

20. The computer-implemented method as recited in Claim 15, wherein:

the evaluation metric is an hFi score calculated based upon results of processing a set of validation summaries using the word-based sub classifier.

21. The computer-implemented method as recited in Claim 15, wherein the predicted probability distribution for the text-based business summary across the plurality of

industry classifications in the hierarchical industry classification structure is a geometric mean of the first probability distribution generated by the word-based sub classifier and the second probability distribution generated by the category-based sub classifier.