METHOD AND SYSTEM FOR MACHINE-LEARNING BASED OPTIMIZATION AND CUSTOMIZATION OF DOCUMENT SIMILARITIES CALCULATION

• US 20120136812A1
• Filed: 11/29/2010
• Published: 05/31/2012
• Est. Priority Date: 11/29/2010
• Status: Abandoned Application

• Current Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Brdiczka, Oliver
• Application Number:
  US12/955,799
• Publication Number:
  US 20120136812A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06V 30/40 Document-oriented image-based pattern recognition
• CPC Class Codes: G06V 30/418 Document matching, e.g. of document images

Abstract

One embodiment of the present invention provides a system for optimizing and customizing document-similarity calculation. During operation, the system presents a collection of similar documents to a user, collects feedback on the similarity of the documents from the user, generates generic rules for calculating document similarity, and filters documents with customized similarity calculation based on the feedback provided by the user.

Claims

1. A computer-implemented method for optimizing and customizing document-similarity calculation, the method comprising:

presenting, by a computer, a collection of similar documents to a user;

collecting feedback on the similarity of the documents from the user;

generating, by the computer, generic rules for calculating document similarity; and

filtering documents with customized similarity calculation based on the feedback provided by the user.

2. The method of claim 1, wherein the user feedback comprises one or more of:

an indication of documents in the collection that are falsely included; and

an indication of additional similar documents not included in the collection.

3. The method of claim 1, further comprising calculating the document similarity by:

extracting a number of semantic entities from the documents; and

calculating a similarity measure between the documents based on inverse document frequency (IDF) values of the extracted semantic entities.

4. The method of claim 1, wherein generating the generic rules for calculating document similarity comprises:

extracting features from a respective document and its related documents based on the collected user feedback; and

applying machine-learning techniques to generate rules based on the extracted features.

5. The method of claim 4, wherein the extracted features of the respective document and its related documents comprise one or more of:

a similarity rank of the related documents;

a document weight of respective and related documents;

an entity occurrence magnitude of respective and related documents;

an entity occurrence average of respective and related documents;

a number of shared entities among respective and related documents;

an average entity weight of the shared entities among respective and related documents;

a maximum entity weight of the shared entities among respective and related documents;

a minimum entity weight of the shared entities among respective and related documents;

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the shared entities among respective and related documents;

a number of complementary (none-shared) entities in respective and related documents;

an average entity weight of the complementary entities in respective and related documents;

a maximum entity weight of the complementary entities in respective and related documents;

a minimum entity weight of the complementary entities in respective and related documents; and

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the complementary entities in respective and related documents.

6. The method of claim 1, further comprising generating a decision tree for calculating document similarity using supervised machine learning.

7. The method of claim 1, wherein filtering documents with customized similarity calculation for a user comprises:

extracting features from a respective document and its related documents based on the feedback provided by the user; and

applying machine-learning techniques to generate filtering rules based on the extracted features.

8. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

presenting, by a computer, a collection of similar documents to a user;

collecting feedback on the similarity of the documents from the user;

generating, by the computer, generic rules for calculating document similarity; and

filtering documents with customized similarity calculation based on the feedback provided by the user.

9. The computer-readable storage medium of claim 8, wherein the user feedback comprises one or more of:

an indication of documents in the collection that are falsely included; and

an indication of additional similar documents not included in the collection.

10. The computer-readable storage medium of claim 8, wherein the method further comprises calculating the document similarity by:

extracting a number of semantic entities from the documents; and

calculating a similarity measure between the documents based on inverse document frequency (IDF) values of the extracted semantic entities.

11. The computer-readable storage medium of claim 8, wherein generating the generic rules for calculating document similarity comprises:

extracting features from a respective document and its related documents based on the collected user feedback; and

applying machine-learning techniques to generate rules based on the extracted features.

12. The computer-readable storage medium of claim 11, wherein the extracted features of the respective document and its related documents comprise one or more of:

a similarity rank of the related documents;

a document weight of respective and related documents;

an entity occurrence magnitude of respective and related documents;

an entity occurrence average of respective and related documents;

a number of shared entities among respective and related documents;

an average entity weight of the shared entities among respective and related documents;

a maximum entity weight of the shared entities among respective and related documents;

a minimum entity weight of the shared entities among respective and related documents;

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the shared entities among respective and related documents;

a number of complementary (none-shared) entities in respective and related documents;

an average entity weight of the complementary entities in respective and related documents;

a maximum entity weight of the complementary entities in respective and related documents;

a minimum entity weight of the complementary entities in respective and related documents; and

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the complementary entities in respective and related documents.

13. The computer-readable storage medium of claim 8, wherein the method further comprises generating a decision tree for calculating document similarity using supervised machine learning.

14. The computer-readable storage medium of claim 8, wherein filtering documents with customized similarity calculation for a user comprises:

extracting features from a respective document and its related documents based on the feedback provided by the user; and

applying machine-learning techniques to generate filtering rules based on the extracted features.

15. A system, comprising:

a presentation mechanism configured to present a collection of similar documents to a user;

a feedback-collecting mechanism configured to collect feedback on the similarity of the documents from the user;

a rule-generating mechanism configured to generate generic rules for calculating document similarity; and

a filtering mechanism configured to filter documents with customized similarity calculation based on the feedback provided by the user.

16. The system of claim 15, wherein the user feedback comprises one or more of:

an indication of documents in the collection that are falsely included; and

an indication of additional similar documents not included in the collection.

17. The system of claim 15, further comprising a calculation mechanism configured to calculate the document similarity by:

extracting a number of semantic entities from the documents; and

calculating a similarity measure between the documents based on inverse document frequency (IDF) values of the extracted semantic entities.

18. The system of claim 15, wherein while generating the generic rules for calculating document similarity, the rule-generation mechanism is configured to:

extract features from a respective document and its related documents based on the collected user feedback; and

apply machine-learning techniques to generate rules based on the extracted features.

19. The system of claim 18, wherein the extracted features of the respective document and its related documents comprise one or more of:

a similarity rank of the related documents;

a document weight of respective and related documents;

an entity occurrence magnitude of respective and related documents;

an entity occurrence average of respective and related documents;

a number of shared entities among respective and related documents;

an average entity weight of the shared entities among respective and related documents;

a maximum entity weight of the shared entities among respective and related documents;

a minimum entity weight of the shared entities among respective and related documents;

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the shared entities among respective and related documents;

a number of complementary (none-shared) entities in respective and related documents;

an average entity weight of the complementary entities in respective and related documents;

a maximum entity weight of the complementary entities in respective and related documents;

a minimum entity weight of the complementary entities in respective and related documents; and

a typed number, average entity weight, minimum entity weight, and maximum entity weight of the complementary entities in respective and related documents.

20. The system of claim 15, further comprising a generating mechanism configured to generate a decision tree for calculating document similarity using supervised machine learning.

21. The system of claim 15, wherein while filtering documents with customized similarity calculation, the filtering mechanism is configured to:

extract features from a respective document and its related documents based on the feedback provided by the user; and

apply machine-learning techniques to generate filtering rules based on the extracted features.