METHOD AND APPARATUS TO BUILD A COMMON CLASSIFICATION SYSTEM ACROSS MULTIPLE CONTENT ENTITIES

• US 20150324459A1
• Filed: 05/09/2014
• Published: 11/12/2015
• Est. Priority Date: 05/09/2014
• Status: Abandoned Application

• Current Assignee: Chegg Incorporated
• Original Assignee: Chegg Incorporated
• Inventors: Sri, Paul Chris, Le Chevalier, Vincent, Chhichhia, Charmy
• Application Number:
  US14/274,189
• Publication Number:
  US 20150324459A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00

Abstract

A content classification system classifies documents of a plurality of content entities into a hierarchical discipline structure. The content classification system receives a set of taxonomic labels collectively defining a hierarchical taxonomy and a plurality of documents. Each document is associated with one of the content entities. The content classification system extracts features from the received documents. A learned model is generated for assigning taxonomic labels to documents associated with a representative content entity using the features extracted from documents associated with the representative content entity. The content classification system assigns one or more taxonomic labels to each document of the other content entities using the learned model applied to the features extracted from the respective document. The documents of the plurality of content entities are classified based on the assigned taxonomic labels.

Claims

1. A method for classifying documents of a plurality of content entities into a hierarchical discipline structure in a content management system, the method comprising:

accessing a set of taxonomic labels, the taxonomic labels collectively defining a hierarchical taxonomy;

receiving a plurality of documents, each document associated with one of the content entities;

extracting features of the received documents;

generating by a content classification system, a learned model for assigning taxonomic labels to documents associated with a representative content entity using the features extracted from documents associated with the representative content entity;

assigning, by the content classification system, one or more taxonomic labels to each document of the other content entities using the learned model applied to the features extracted from the respective document; and

classifying the documents of the plurality of content entities based on the assigned taxonomic labels.

2. The method of claim 1, further comprising:

recommending a document to a user based on the classification of the documents.

3. The method of claim 1, further comprising:

for each of the plurality of content entities, determining a feature overlap between features extracted from documents associated with the content entity and features extracted from documents associated with the other content entities; and

selecting the representative content entity responsive to the documents associated with the representative content entity having a high feature overlap with documents associated with the other content entities.

4. The method of claim 3, wherein determining the feature overlap comprises:

predicting for each of a plurality of documents, a content entity associated with the document; and

for a document associated with a first content entity, responsive to predicting the document as being associated with a second content entity, identifying a feature overlap between the first content entity and the second content entity.

5. The method of claim 1, further comprising:

for one of the content entities, receiving judgments from evaluators of the taxonomic labels assigned to a subset of the documents associated with the content entity; and

modifying the learned model based on the received evaluator judgments.

6. The method of claim 5, further comprising:

determining a confidence score for each evaluator judgment;

wherein modifying the learned model based on the received evaluator judgments comprises retraining the learned model using evaluator judgments with confidence scores above a threshold.

7. The method of claim 1, wherein the set of taxonomic labels includes labels for each of a plurality of categories and a plurality of subjects within each category, and wherein assigning the one or more taxonomic labels to each document comprises assigning a category label and a subject label to each document.

8. The method of claim 1, wherein the plurality of content entities include at least two selected from the group consisting of textbooks, jobs, academic courses, sets of questions and answers, and academic video transcriptions.

9. The method of claim 1, wherein the features extracted from each received document include at least one selected from the group consisting of a title of the document, an author of the document, keywords of the document, and a description of the document.

10. The method of claim 1, further comprising:

receiving a plurality of documents associated with a new content entity;

assigning, by the content classification system, one or more taxonomic labels to each document of the new content entity using the learned model applied to the features extracted from the respective document; and

classifying the documents associated with the new content entity based on the assigned taxonomic labels.

11. The method of claim 1, further comprising:

generating a user interface for display to a user, the user interface including a representation of a hierarchy of the taxonomic labels and a plurality of the documents associated with each of the taxonomic labels in the hierarchy.

12. A non-transitory computer readable storage medium storing computer program instructions for classifying documents of a plurality of content entities into a hierarchical discipline structure, the computer program instructions when executed by a processor causing the processor to:

access a set of taxonomic labels, the taxonomic labels collectively defining a hierarchical taxonomy;

receive a plurality of documents, each document associated with one of the content entities;

extract features of the received documents;

generate a learned model for assigning taxonomic labels to documents associated with a representative content entity using the features extracted from documents associated with the representative content entity;

assign one or more taxonomic labels to each document of the other content entities using the learned model applied to the features extracted from the respective document; and

classify the documents of the plurality of content entities based on the assigned taxonomic labels.

13. The non-transitory computer readable storage medium of claim 12, further comprising computer program instructions that when executed by a processor cause the processor to:

recommend a document to a user based on the classification of the documents.

14. The non-transitory computer readable storage medium of claim 12, further comprising computer program instructions that when executed by a processor cause the processor to:

for each of the plurality of content entities, determine a feature overlap between features extracted from documents associated with the content entity and features extracted from documents associated with the other content entities; and

select the representative content entity responsive to the documents associated with the representative content entity having a high feature overlap with documents associated with the other content entities.

15. The non-transitory computer readable storage medium of claim 14, wherein the computer program instructions causing the processor to determine the feature overlap further cause the processor to:

predicting for each of a plurality of documents, a content entity associated with the document; and

for a document associated with a first content entity, responsive to predicting the document as being associated with a second content entity, identifying a feature overlap between the first content entity and the second content entity.

16. The non-transitory computer readable storage medium of claim 12, further comprising computer program instructions that when executed by a processor cause the processor to:

for one of the content entities, receive judgments from evaluators of the taxonomic labels assigned to a subset of the documents associated with the content entity; and

modify the learned model based on the received evaluator judgments.

17. The non-transitory computer readable storage medium of claim 16, further comprising computer program instructions that when executed by a processor cause the processor to:

determine a confidence score for each evaluator judgment;

wherein the computer program instructions causing the processor to modify the learned model based on the received evaluator judgments further cause the processor to retrain the learned model using evaluator judgments with confidence scores above a threshold.

18. The non-transitory computer readable storage medium of claim 12, wherein the set of taxonomic labels includes labels for each of a plurality of categories and a plurality of subjects within each category, and wherein the computer program instructions causing the processor to assign the one or more taxonomic labels to each document further cause the processor to assign a category label and a subject label to each document.

19. The non-transitory computer readable storage medium of claim 12, wherein the plurality of content entities include at least two selected from the group consisting of textbooks, jobs, academic courses, sets of questions and answers, and academic video transcriptions.

20. The non-transitory computer readable storage medium of claim 12, wherein the features extracted from each received document include at least one selected from the group consisting of a title of the document, an author of the document, keywords of the document, and a description of the document.