System and method for providing a classification suggestion for concepts

• US 8,515,958 B2
• Filed: 07/27/2010
• Issued: 08/20/2013
• Est. Priority Date: 07/28/2009
• Status: Active Grant

• Current Assignee: Nuix North America Inc. (Nuix Ltd.)
• Original Assignee: FTI Consulting Incorporated
• Inventors: Knight, William C.
• Primary Examiner(s): Choi, Yuk Ting
• Application Number:
  US12/844,785
• Publication Number:
  US 20110029529A1
• Time in Patent Office: 1,120 Days
• Field of Search: None
• US Class Current: 707/737
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A system and method for providing a classification suggestion for concepts is provided. A corpus of concepts including reference concepts each associated with a classification and uncoded concepts are maintained. A cluster of uncoded concepts and reference concepts is provided. A neighborhood of reference concepts in the cluster is determined for at least one of the uncoded concepts. A classification of the neighborhood is determined using a classifier. The classification of the neighborhood is suggested as a classification for the at least one uncoded concept.

Claims

1. A method for providing a classification suggestion for concepts, comprising the steps of:

maintaining a corpus of reference concepts, each associated with a classification;

obtaining uncoded concepts, each comprising a collection of one or more nouns and noun phrases with a common semantic meaning that are extracted from one or more documents;

generating a cluster of the uncoded concepts and at least one of the reference concepts;

determining a neighborhood of one or more reference concepts in the cluster for at least one of the uncoded concepts;

determining a classification for the neighborhood of the reference concepts using a classifier;

suggesting the classification of the neighborhood as a classification for the at least one uncoded concept;

receiving a classification code for the at least one uncoded concept from a reviewer;

identifying a discordance between the received classification code for the at least one uncoded concept and the suggested classification code for the at least one uncoded concept when the received classification code is different from the suggested classification code;

assigning an identifier to each of the received classification code and the suggested classification code; and

displaying the at least one uncoded concept with the identifier for the received classification code and the identifier for the suggested classification code,

wherein the steps are performed by a suitably programmed computer.

2. The method according to claim 1, further comprising:

applying the suggested classification to the one or more documents associated with the at least one uncoded concept concept.

3. The method according to claim 1, further comprising:

determining a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept; and

assigning the classification of the reference concept in the neighborhood with the closest distance metric as the classification of the neighborhood.

4. The method according to claim 1, further comprising:

determining a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept;

summing the distance metrics of the reference concepts associated with the same classification;

averaging the sums of the distance metrics in each classification; and

assigning the classification of the reference concepts in the neighborhood with the closest average distance metric as the classification of the neighborhood.

5. The method according to claim 1, further comprising:

calculating a vote for each reference concept in the neighborhood; and

assigning the classification of the reference concepts in the neighborhood with the highest calculated vote total as the classification of the neighborhood.

6. The method according to claim 1, further comprising:

calculating a vote for each reference concept in the neighborhood;

determining a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept;

differentially weighing the votes based on the distance metric; and

assigning the classification of the reference concepts in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.

7. The method according to claim 1, further comprising:

providing a confidence level of the suggested classification.

8. The method according to claim 7, further comprising:

displaying the confidence level only when above a confidence level threshold.

9. The method according to claim 1, wherein the neighborhood is determined based on one of inclusion, injection, and nearest neighbor.

10. The method according to claim 1, wherein the classifier is one of minimum distance, minimum average distance, maximum counts. and distance weighted maximum count.

11. A system for providing a classification suggestion for concepts, comprising:

a database to store a corpus of reference concepts, each associated with a classification and uncoded concepts, each comprising a collection of one or more nouns and noun phrases with a common semantic meaning that are extracted from one or more documents;

a clustering engine to generate a cluster of uncoded concepts and one or more of the reference concepts; and

a processor to execute modules, comprising;

a neighborhood module to determine a neighborhood of one or more reference concepts in the cluster for at least one of the uncoded concepts;

a classification module to determine a classification for the neighborhood of the reference concepts using a classifier;

a suggestion module to suggest the classification of the neighborhood as a classification for the at least one uncoded concept;

a receipt module to receive a classification code for the at least one uncoded concept from a reviewer;

a discordance module to identify a discordance between the received classification code for the at least one uncoded concept and the suggested classification code for the at least one uncoded concept when the received classification code is different from the suggested classification code;

an identifier module to assign an identifier to each of the received classification code and the suggested classification code; and

a display module to display the at least one uncoded concept with the identifier for the received classification code and the identifier for the suggested classification code.

12. The system according to claim 11, further comprising:

a marking module to apply the suggested classification to the one or more documents associated with the at least one uncoded concept.

13. The system according to claim 11, further comprising:

a distance module to determine a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept; and

an assign module to assign the classification of the reference concept in the neighborhood with the closest distance metric as the classification of the neighborhood.

14. The system according to claim 11, further comprising:

a distance module to determine a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept;

a calculation module to sum the distance metrics of the reference concepts associated with the same classification and to average the sums of the distance metrics in each classification; and

an assign module to assign the classification of the reference concepts in the neighborhood with the closest average distance metric as the classification of the neighborhood.

15. The system according to claim 11, further comprising:

a vote module to calculate a vote for each reference concept in the neighborhood; and

an assign module to assign the classification of the reference concepts in the neighborhood with the highest calculated vote total as the classification of the neighborhood.

16. The system according to claim 11, further comprising:

a vote module to calculate a vote for each reference concept in the neighborhood;

a distance module to determine a distance metric based on the similarity of each reference concept in the neighborhood to the at least one uncoded concept;

a weight module to differentially weigh the votes based on the distance metric; and

an assign module to assign the classification of the reference concepts in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.

17. The system according to claim 11, further comprising:

a confidence module to provide a confidence level of the suggested classification.

18. The system according to claim 17, further comprising a display to display the confidence level only when above a confidence level threshold.

19. The system according to claim 11, wherein the neighborhood is determined based on one of inclusion, injection, and nearest neighbor.

20. The system according to claim 11, wherein the classifier is one of minimum distance, minimum average distance, maximum counts, and distance weighted maximum count.