Alignment of Metadata

• US 20120303643A1
• Filed: 05/26/2011
• Published: 11/29/2012
• Est. Priority Date: 05/26/2011
• Status: Abandoned Application

• Current Assignee: Cxense ASA
• Original Assignee: Cxense ASA
• Inventors: Lau, Raymond
• Application Number:
  US13/116,669
• Publication Number:
  US 20120303643A1
• Time in Patent Office: Days
• US Class Current: 707/756
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 40/106 Display of layout of documents; Previewing
• CPC Class Codes: G06F 40/166 Editing, e.g. inserting or deleting
• CPC Class Codes: G06F 40/45 Example-based machine translation; Alignment

Abstract

Methods and apparatus, including computer program products, for alignment of metadata. A method includes receiving two or more variations of an underlying piece of content, each piece of content including metadata, using a text alignment technique to correlate the metadata of the two or more variations, and merging multiple sets of the metadata into one multi-track set from the correlation.

Claims

1. A method comprising:

receiving two or more variations of an underlying piece of content, each variation including metadata;

using a text alignment technique to correlate the metadata of the two or more variations; and

merging multiple sets of the metadata into one multi-track set from the correlation.

2. The method of claim 1 wherein the content includes one or more of digital text, digital audio and digital video.

3. The method of claim 1 wherein the text alignment technique is a dynamic programming process optimizing a metric.

4. The method of claim 3 wherein the metric is a metric that minimizes a number of word substitutions, insertions and deletions.

5. The method of claim 3 wherein the metric is a metric that weights different words differently.

6. The method of claim 3 wherein the metric assigns different penalties to different errors and minimizes a total weighted penalty.

7. The method of claim 3 wherein the metric is calculated in conjunction with natural language processing.

8. The method of claim 3 wherein the metric is calculated using a Viterbi dynamic programming process for finding the most likely sequence of hidden states.

9. The method of claim 1 wherein receiving two or more variations of the underlying piece of content further comprises applying pattern-based normalization on the two or more variations.

10. The method of claim 9 wherein applying pattern-based normalization comprises removing time stamps from closed-captioning.

11. The method of claim 1 wherein the one multi-track set includes external non-aligned metadata.

12. The method of claim 11 wherein the external non-aligned metadata is selected based on aligned metadata.

13. The method of claim 1 wherein the content is digital audio.

14. The method of claim 13 wherein speech-to-text is performed on the digital audio.

15. The method of claim 1 wherein the text alignment technique comprises text aligning to one or more time alignments to align the metadata of the two or more variations.

16. An apparatus comprising:

a local computing system linked to a network of interconnected computer systems, the local computing system comprising a processor, a memory and a storage device;

the memory comprising an operating system and a metadata alignment process, the metadata alignment process comprising;

receiving two or more variations of an underlying piece of content, each piece of content including metadata;

using a text alignment technique to correlate the metadata of the two or more variations; and

merging multiple sets of the metadata into one multi-track set from the correlation.

17. The apparatus of claim 16 wherein the content includes one or more of digital text, digital audio and digital video.

18. The apparatus of claim 16 wherein the text alignment technique is a dynamic programming process optimizing a metric.

19. The apparatus of claim 18 wherein the metric is a metric that minimizes a number of word substitutions, insertions and deletions.

20. The apparatus of claim 18 wherein the metric is a metric that weights different words differently.

21. The apparatus of claim 18 wherein the metric is calculated in conjunction with natural language processing.

22. The apparatus of claim 18 wherein the metric is calculated using a Viterbi dynamic programming process for finding the most likely sequence of hidden states.

23. The apparatus of claim 16 wherein receiving two variations of the underlying piece of content further comprises applying pattern-based normalization on the two variations.

24. The apparatus of claim 23 wherein applying pattern-based normalization comprises removing time stamps from closed-captioning.

25. The apparatus of claim 16 wherein the one multi-track set includes external non-aligned metadata.

26. The apparatus of claim 25 wherein the external non-aligned metadata is selected based on aligned metadata.

27. The apparatus of claim 16 wherein the content is digital audio.

28. The apparatus of claim 27 wherein speech-to-text is performed on the digital audio.

29. A method comprising:

receiving variations of an underlying piece of content, each piece of content including metadata;

using a text alignment technique to correlate the metadata of a first variation to a third variation, the correlated metadata including timestamps;

using the text alignment technique to correlate the metadata of a second variation to the third variation, the correlated metadata including timestamps; and

merging the correlated metadata into one multi-track set.

30. The method of claim 29 wherein the content includes one or more of digital text, digital audio and digital video.

31. The method of claim 29 wherein the text alignment technique is a dynamic programming process optimizing a metric.

32. The method of claim 29 wherein the content is digital audio.

33. The method of claim 32 wherein speech-to-text is performed on the digital audio.