Multi-label content recategorization
View Patent ↗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.
1. A computing apparatus, comprising:
a hardware platform comprising a processor and a memory; and
one or more tangible, non-transitory computer-readable mediums having instructions to provide a two-phase classification engine to:
in a first phase, receive a clean multi-labeled dataset comprising a plurality of documents, each assigned to one or more categories from a set of fixed categories;
receive an unclean multi-labeled dataset, wherein at least some objects of the unclean multi-labeled dataset belong to overlapping classes, wherein the probability that a document belongs to the overlapping classes is approximately equal;
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 {circumflex over (l)}; and
in a second phase, compute from the recategorized and cleansed dataset a probability difference between l and {circumflex over (l)} for j, and take l to be correct if the difference is less than or equal to a threshold.
2. The computing apparatus of claim 1 , wherein the two-phase 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 two-phase classification engine is further to use the training dataset to build a support vector regression model to predict a number of labels to associate with document j.
4. The computing apparatus of claim 3 , wherein the two-phase 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 two-phase classification engine.
5. The computing apparatus of claim 1 , wherein producing the recategorized and cleansed 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 α.
6. The computing apparatus of claim 1 , wherein producing the recategorized and cleansed dataset from the unclean multi-labeled dataset further comprises computing a set of predicted labels Ŝ for document j.
7. The computing apparatus of claim 6 , wherein producing the recategorized and cleansed 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 cleansed 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 T 1 .
9. The computing apparatus of claim 7 , wherein producing the recategorized and cleansed 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 cleansed 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 T 1 .
11. The computing apparatus of claim 1 , wherein the two-phase classification engine is further to build a classifier from the recategorized and cleansed dataset.
12. The computing apparatus of claim 11 , wherein the two-phase 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 two-phase classification engine to:
in a first phase, receive a clean multi-labeled dataset comprising a plurality of documents, each assigned to one or more categories from a set of fixed categories;
receive an unclean multi-labeled dataset, wherein at least some objects of the unclean multi-labeled dataset belong to overlapping classes, wherein the probability that a document belongs to the overlapping classes is approximately equal;
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; and
in a second phase, compute from the recategorized and cleansed dataset a probability difference between l and {circumflex over (l)} for j, and take l to be correct if the difference is less than or equal to a threshold.
14. The one or more tangible, non-transitory computer-readable mediums of claim 13 , wherein the two-phase 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 two-phase classification engine is further to use the training dataset to build a support vector regression model to predict a number of labels to associate with document j.
16. The one or more tangible, non-transitory computer-readable mediums of claim 15 , wherein the two-phase 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 two-phase classification engine.
17. The one or more tangible, non-transitory computer-readable mediums of claim 13 , wherein producing the recategorized and cleansed 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 α.
18. The one or more tangible, non-transitory computer-readable mediums of claim 13 , wherein producing the recategorized and cleansed dataset from the unclean multi-labeled dataset further comprises computing a set of predicted labels Ŝ for document j.
19. The one or more tangible, non-transitory computer-readable mediums of claim 18 , wherein producing the recategorized and cleansed 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 cleansed 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 T 1 .
21. The one or more tangible, non-transitory computer-readable mediums of claim 19 , wherein producing the recategorized and cleansed 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 cleansed 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 S that have a probability greater than a threshold T 1 .
23. A computer-implemented method of providing two-phase multi-label content recategorization, comprising:
in a first phase, receiving a clean multi-labeled dataset comprising a plurality of documents, each assigned to one or more categories from a set of fixed categories;
receiving an unclean multi-labeled dataset, wherein at least some objects of the unclean multi-labeled dataset belong to overlapping classes, wherein the probability that a document belongs to the overlapping classes is approximately equal;
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; and
in a second phase, computing from the recategorized and cleansed dataset a probability difference between l and {circumflex over (l)} for j, and take l to be correct if the difference is less than or equal to a threshold.
24. The method of claim 23 , wherein producing the recategorized and cleansed 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 α.
25. The method of claim 23 , wherein producing the recategorized and cleansed dataset from the unclean multi-labeled dataset further comprises comparing Ŝ to a set of existing labels S.