MULTI-LABEL CONTENT RECATEGORIZATION

• US 20170177627A1
• Filed: 12/22/2015
• Published: 06/22/2017
• Est. Priority Date: 12/22/2015
• Status: Active Grant

• Current Assignee: Musarubra US LLC (Musarubra US SellCo LLC)
• Original Assignee: Intel IP Corporation (Intel Corporation)
• Inventors: Olinsky, Craig Philip, Singh, Nidhi, Paramasivam, Thamizhannal
• Granted Patent: US 10,691,739 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

In an example, there is disclosed a computing apparatus, including one or more logic elements, including at least one hardware logic element, comprising a classification engine to: receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories; receive an unclean multi-labeled dataset; and produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l. There is also disclosed a method of providing a classification engine.

Claims

1. A computing apparatus, comprising:

one or more logic elements, including at least one hardware logic element, comprising a classification engine to;

receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories;

receive an unclean multi-labeled dataset; and

produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l.

2. The computing apparatus of claim 1, wherein the classification engine is further to divide at least part of the clean multi-labeled dataset into a training dataset.

3. The computing apparatus of claim 2, wherein the classification engine is further to use the training set to build a support vector regression model to predict a number of labels to associate with j.

4. The computing apparatus of claim 3, wherein the classification engine is further to divide at least part of the clean multi-labeled dataset into a validation set, and to use the validation set to tune the classification engine.

5. The computing apparatus of claim 1, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises:

receiving a probability threshold a for a number of labels;

computing a probability for {circumflex over (l)}; and

determining that the probability for {circumflex over (l)} is greater than a.

6. The computing apparatus of claim 1, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises computing a set of predicted labels Ŝ

for j.

7. The computing apparatus of claim 6, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises comparing Ŝ

to a set of existing labels S.

8. The computing apparatus of claim 7, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is partly but not fully contained in S, and replacing S with labels unique to Ŝ

that have a probability greater than a threshold T¹.

9. The computing apparatus of claim 7, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is fully contained in S, and replacing S with Ŝ

.

10. The computing apparatus of claim 7, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is not contained in S, and replacing S with labels common to Ŝ and

S, along with labels unique to Ŝ

that have a probability greater than a threshold T¹.

11. The computing apparatus of claim 1, wherein the classification engine is further to build a classifier from the recategorized and cleansed dataset.

12. The computing apparatus of claim 1, wherein the classification engine is further to compare a precision of the classifier to a precision of a prior classifier.

13. One or more tangible, non-transitory computer-readable mediums having stored thereon executable instructions for providing a classification engine to:

receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories;

receive an unclean multi-labeled dataset; and

produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l.

14. The one or more tangible, non-transitory computer-readable mediums of claim 13, wherein the classification engine is further to divide at least part of the clean multi-labeled dataset into a training dataset.

15. The one or more tangible, non-transitory computer-readable mediums of claim 14, wherein the classification engine is further to use the training set to build a support vector regression model to predict a number of labels to associate with j.

16. The one or more tangible, non-transitory computer-readable mediums of claim 15, wherein the classification engine is further to divide at least part of the clean multi-labeled dataset into a validation set, and to use the validation set to tune the classification engine.

17. The one or more tangible, non-transitory computer-readable mediums of claim 13, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises:

receiving a probability threshold a for a number of labels;

computing a probability for {circumflex over (l)}; and

determining that the probability for {circumflex over (l)} is greater than a.

18. The one or more tangible, non-transitory computer-readable mediums of claim 13, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises computing a set of predicted labels Ŝ

for j.

19. The one or more tangible, non-transitory computer-readable mediums of claim 18, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises comparing Ŝ

to a set of existing labels S.

20. The one or more tangible, non-transitory computer-readable mediums of claim 19, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is partly but not fully contained in S, and replacing S with labels unique to Ŝ

that have a probability greater than a threshold T¹.

21. The one or more tangible, non-transitory computer-readable mediums of claim 19, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is fully contained in S, and replacing S with Ŝ

.

22. The one or more tangible, non-transitory computer-readable mediums of claim 19, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises determining that Ŝ

is not contained in S, and replacing S with labels common to Ŝ and

S, along with labels unique to Ŝ

that have a probability greater than a threshold T¹.

23. A computer-implemented method of providing multi-label content recategorization, comprising:

receiving a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories;

receiving an unclean multi-labeled dataset; and

producing a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l.

24. The method of claim 23, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises:

receiving a probability threshold a for a number of labels;

computing a probability for {circumflex over (l)}; and

determining that the probability for {circumflex over (l)} is greater than a.

25. The method of claim 23, wherein producing the recategorized and cleaned dataset from the unclean multi-labeled dataset further comprises comparing Ŝ

to a set of existing labels S.