MODEL-BASED IDENTIFICATION OF RELEVANT CONTENT

• US 20170075978A1
• Filed: 09/16/2015
• Published: 03/16/2017
• Est. Priority Date: 09/16/2015
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Zhang, Yongzheng, Kuan, Chi-Yi, Zheng, Yi
• Application Number:
  US14/856,306
• Publication Number:
  US 20170075978A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

The disclosed embodiments provide a system for processing data. During operation, the system obtains validated training data containing a first set of content items and a first set of relevance tags, wherein the first set of relevance tags is used by one or more domain experts to identify the first set of content items as relevant to one or more topics. Next, the system uses the validated training data to produce a statistical model for classifying a relevance of content to the one or more topics. The system then uses the statistical model to generate a second set of relevance tags for a second set of content items. Finally, the system outputs one or more groupings of the second set of content items by the second set of relevance tags to improve understanding of content related to the one or more topics without requiring a user to manually analyze the second set of content items.

Claims

1. A method, comprising:

obtaining validated training data comprising a first set of content items and a first set of relevance tags, wherein the first set of relevance tags is used by one or more domain experts to identify the first set of content items as relevant to one or more topics;

using the validated training data to produce, by one or more computer systems, a statistical model for classifying a relevance of content to the one or more topics;

using the statistical model to generate, by the one or more computer systems, a second set of relevance tags for a second set of content items; and

outputting, by the one or more computer systems, one or more groupings of the second set of content items by the second set of relevance tags to improve understanding of content related to the one or more topics without requiring a user to manually analyze the second set of content items.

2. The method of claim 1, further comprising:

obtaining a validated subset of the second set of relevance tags for the second set of content items.

3. The method of claim 2, further comprising:

providing the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

using the update to generate a third set of relevance tags for a third set of content items.

4. The method of claim 1, wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises:

generating a set of features from a content item in the first set of content items; and

providing the set of features as input to the statistical model.

5. The method of claim 4, wherein the set of features comprises one or more n-grams from the content item.

6. The method of claim 4, wherein the set of features comprises at least one of:

a number of characters;

a number of capitalized characters; and

a number of special characters.

7. The method of claim 4, wherein the set of features comprises at least one of:

a number of proper nouns;

a number of emoticons;

a number of words; and

a number of sentences.

8. The method of claim 4, wherein the set of features comprises at least one of:

an average number of words in a sentence;

a percentage of special characters;

a percentage of emoticon characters; and

a number of Uniform Resource Locators.

9. The method of claim 4, wherein the set of features comprises a topic related to social media.

10. The method of claim 1, wherein the one or more topics comprise a product associated with an online professional network.

11. An apparatus, comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to;

obtain validated training data comprising a first set of content items and a first set of relevance tags, wherein the first set of relevance tags is used by one or more domain experts to identify the first set of content items as relevant to one or more topics;

use the validated training data to produce a statistical model for classifying a relevance of content to the one or more topics;

use the statistical model to generate a second set of relevance tags for a second set of content items; and

output one or more groupings of the second set of content items by the second set of relevance tags to improve understanding of content related to the one or more topics without requiring a user to manually analyze the second set of content items.

12. The apparatus of claim 11, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

obtain a validated subset of the second set of relevance tags for the first set of content items;

provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

use the update to generate a third set of relevance tags for a third set of content items.

13. The apparatus of claim 11, wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises:

generating a set of features from a content item in the first set of content items; and

providing the set of features as input to the statistical model.

14. The apparatus of claim 13, wherein the set of features comprises at least one of:

a number of characters;

a number of capitalized characters; and

a number of special characters.

15. The apparatus of claim 13, wherein the set of features comprises at least one of:

a number of proper nouns;

a number of emoticons;

a number of words; and

a number of sentences.

16. The apparatus of claim 13, wherein the set of features comprises at least one of:

an average number of words in a sentence;

a percentage of special characters;

a percentage of emoticon characters; and

a number of Uniform Resource Locators.

17. The apparatus of claim 13, wherein the set of features comprises a topic related to social media.

18. The apparatus of claim 13, wherein the one or more topics comprise a product associated with an online professional network.

19. A system, comprising:

an analysis non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to;

obtain validated training data comprising a first set of content items and a first set of relevance tags, wherein the first set of relevance tags is used by one or more domain experts to identify the first set of content items as relevant to one or more topics;

use the validated training data to produce a statistical model for classifying a relevance of content to the one or more topics; and

use the statistical model to generate a second set of relevance tags for a second set of content items; and

a management non-transitory computer-readable medium comprising instructions that, when executed by the one or more processors, cause the system to output one or more groupings of the second set of content items by the second set of relevance tags to improve understanding of content related to the one or more topics without requiring a user to manually analyze the second set of content items.

20. The system of claim 19, wherein the analysis non-transitory computer-readable medium further instructions that, when executed by the one or more processors, cause the system to:

obtain a validated subset of the second set of relevance tags for the first set of content items;

provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and

use the update to generate a third set of relevance tags for a third set of content items.