SYSTEMS AND METHODS FOR TRAINING AND CLASSIFYING DATA

• US 20140058983A1
• Filed: 08/23/2012
• Published: 02/27/2014
• Est. Priority Date: 08/23/2012
• Status: Active Grant

• Current Assignee: Kira, Inc., ZUVA Incorporated
• Original Assignee: Diligenceengine Incorporated
• Inventors: Alexander, Hudek Karl
• Granted Patent: US 9,158,839 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

A mechanism for training and classifying data is disclosed. The method includes receiving a data set having at least a first annotation and at least a second annotation. The first annotation and the second annotation represent characteristics within the data set. The method also includes determining a first identifier from the first annotation and a second identifier from the second annotation and associating the first identifier to the second identifier to generate a joined identifier. The method also includes computing feature weights and transition weights for the annotated data set based on the at least a first identifier, at least a second identifier, and at least a joined identifier and transitions between each of the first, the second and the joined identifiers. The method further includes receiving a second un-annotated data set and classifying the second data set based on the computed feature weights and the transition weights.

Claims

1. A method comprising:

receiving, by a processing device, a data set, wherein the data set is annotated with at least a first annotation and at least a second annotation, wherein at least the first annotation and the second annotation represent characteristics within the data set;

determining, by the processing device, a first identifier from the first annotation and a second identifier from the second annotation;

associating, by the processing device, the first identifier to the second identifier to generate a joined identifier;

computing, by the processing device, feature weights and transition weights for the annotated data set based on at least the first identifier, at least the second identifier, and at least the joined identifier and transitions between each of the first, the second and the joined identifiers;

receiving, by the processing device, a second data set, wherein the second data set is un-annotated; and

classifying, by the processing device, the second data set based on the computed feature weights and the transition weights.

2. The method of claim 1 further comprising providing the classified second data set to a user.

3. The method of claim 1 wherein the data set and the second data set comprise a plurality of sentences having a plurality of words.

4. The method of claim 3 wherein each of the first annotation and the second annotation comprise at least one word.

5. The method of claim 3 wherein the classifying comprising assigning at least one class label to each of the sentences in the second data set.

6. A system comprising:

a memory;

a processing device coupled to the memory, the processing device configured to;

receive a data set, wherein the data set is annotated with at least a first annotation and at least a second annotation, wherein at least the first annotation and the second annotation represent within the data set;

determine a first identifier from the first annotation and a second identifier from the second annotation;

associate, the first identifier to the second identifier to generate a joined identifier;

compute feature weights and transition weights for the annotated data set based on at least the first identifier, at least the second identifier, and at least the joined identifier and transitions between each of the first, the second and the joined identifiers;

receive a second data set, wherein the second data set is un-annotated; and

classify the second data set based on the computed feature weights and the transition weights.

7. The system of claim 6 wherein the processing device is further configured to provide the classified second data set to a user.

8. The system of claim 6 wherein the data set and the second data set comprise a plurality of sentences having a plurality of words.

9. The system of claim 8 wherein each of the first annotation and the second annotation comprise at least one word.

10. The system of claim 8 wherein the classify comprise assign at least one class label to each of the sentences in the second data set.

11. A non-transitory machine-readable storage medium including data that, when accessed by a machine, cause the machine to perform a method comprising:

receiving, by a processing device, a data set, wherein the data set is annotated with at least a first annotation and at least a second annotation, wherein at least the first annotation and the second annotation represent characteristics within the data set;

determining, by the processing device, a first identifier from the first annotation and a second identifier from the second annotation;

associating, by the processing device, the first identifier to the second identifier to generate a joined identifier;

computing, by the processing device, feature weights and transition weights for the annotated data set based on at least the first identifier, at least the second identifier, and at least the joined identifier and transitions between each of the first, the second and the joined identifiers;

receiving, by the processing device, a second data set, wherein the second data set is un-annotated; and

classifying, by the processing device, the second data set based on the computed feature weights and the transition weights.

12. The non-transitory machine-readable storage medium of claim 11 wherein the method further comprises providing the classified second data set to a user.

13. The non-transitory machine-readable storage medium of claim 11 wherein the data set and the second data set comprise a plurality of sentences having a plurality of words.

14. The non-transitory machine-readable storage medium of claim 13 each of the first annotation and the second annotation comprise at least one word.

15. The non-transitory machine-readable storage medium of claim 14 wherein each of the first annotation and the second annotation comprise at least one word.

16. The non-transitory machine-readable storage medium of claim 14 wherein the classifying comprising assigning at least one class label to each of the sentences in the second data set.