System and method for classifying text

• US 7,752,159 B2
• Filed: 08/23/2007
• Issued: 07/06/2010
• Est. Priority Date: 01/03/2001
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Cohen, Dani, Hajaj, Nissan, Magdalen, Yosemina, Nelken, Yoram
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): FERNANDEZ RIVAS, OMAR F
• Application Number:
  US11/843,909
• Publication Number:
  US 20070294199A1
• Time in Patent Office: 1,048 Days
• Field of Search: 706/14, 706/15, 706 17- 18, 706 20- 21, 706 45- 48, 706/55, 706/61, 715/200, 715/202, 715/205, 715/210, 715/221, 715224-226, 715/701, 715/707, 715/708, 707/1, 707/3, 707/5
• US Class Current: 706/62
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

A system and method for classifying text includes a pre-processor, a knowledge base, and a statistical engine. The pre-processor identifies concepts in the text and creates a structured text object that contains the concepts. The structured text object is then passed to a statistical engine, which applies statistical information provided in nodes of a knowledge base to the structured text object in order to calculate a set of match scores, each match score representing the relevance of the text to an associated one of a plurality of predefined categories. The pre-processor may be implemented in the form of an interpreter which selects and executes a script that includes language- and scenario-specific instructions for performing linguistic and semantic analysis of the text.

Claims

1. A method of classifying text on a computer for electronic communication management in a contact center, comprising steps of:

analyzing, in the computer, text from an electronic communication received from a customer to determine the customer'"'"'s intent by identifying concepts in the text and building a concept model containing the concepts;

providing, in the computer, a knowledge base having a plurality of nodes including a set of learning nodes, each of the learning nodes being provided with statistical information for determining a relevance of the text to a category associated with the node;

calculating, in the computer, a set of match scores for the concept model by using the knowledge base, each match score of the set of match scores indicating the relevance of the text to a category associated with a node of the knowledge base, the category including at least one suggested action to be performed in response to the electronic communication, wherein the suggested action is representative of the relevance of the text to the category, and the suggested action includes generating an automatic response to the customer or routing the electronic communication to an agent to generate an assisted response to the customer; and

performing, in the computer, the suggested action in response to the electronic communication based on the calculated set of match scores, in order to improve the response of the contact center to the electronic communications received from customers by the contact center.

2. The method of claim 1, wherein the text includes a plurality of fields, a first subset of the plurality of fields consisting of unstructured data and a second subset of the plurality of fields consisting of structured data.

3. The method of claim 1, wherein the plurality of nodes further includes a set of rule-based nodes.

4. The method of claim 1, wherein the plurality of nodes are organized into a tree structure.

5. The method of claim 1, further comprising a step of calibrating match scores to values of an operational parameter.

6. The method of claim 5, wherein the operational parameter is selected from a group consisting of precision and recall.

7. The method of claim 1, further comprising a step of selecting an appropriate script from a plurality of scripts and executing the selected script to identify concepts in the text.

8. The method of claim 7, wherein the step of selecting an appropriate script from a plurality of scripts includes identifying a language in which the text is written, and selecting the script corresponding to the identified language.

9. The method of claim 1, further comprising a step of using real-time feedback to modify the statistical information provided to one or more learning nodes of the set of learning nodes.

10. The method of claim 9, wherein the real-time feedback comprises a response of a human agent to the relevance of the text to associated categories based upon the set of match scores.

11. The method of claim 9, wherein the real-time feedback comprises a reply to the suggested action, the suggested action comprising a suggested response or a link to a web-resource.

12. The method of claim 9, wherein the step of using real-time feedback to modify the statistical information comprises a step of modifying weights assigned to the statistical information.

13. A computer program product storing one or more computer-readable instructions executed by a computer that results in the computer performing a method of classifying text on the computer for electronic communication management in a contact center, the method comprising steps of:

analyzing, in the computer, text from an electronic communication received from a customer to determine the customer'"'"'s intent by identifying concepts in the text and building a concept model containing the concepts;

providing, in the computer, a knowledge base having a plurality of nodes including a set of learning nodes, each of the learning nodes being provided with statistical information for determining a relevance of the text to a category associated with the node;

calculating, in the computer, a set of match scores for the concept model by using the knowledge base, each match score of the set of match scores indicating the relevance of the text to a category associated with a node of the knowledge base, the category including at least one suggested action to be performed in response to the electronic communication, wherein the suggested action is representative of the relevance of the text to the category, and the suggested action includes generating an automatic response to the customer or routing the electronic communication to an agent to generate an assisted response to the customer; and

performing, in the computer, the suggested action in response to the electronic communication based on the calculated set of match scores, in order to improve the response of the contact center to the electronic communications received from customers by the contact center.

14. The computer program product of claim 13, wherein the text includes a plurality of fields, a first subset of the plurality of fields consisting of unstructured data and a second subset of the plurality of fields consisting of structured data.

15. The computer program product of claim 13, wherein the plurality of nodes further includes a set of rule-based nodes.

16. The computer program product of claim 13, wherein the plurality of nodes are organized into a tree structure.

17. The computer program product of claim 13, further comprising a step of calibrating match scores to values of an operational parameter.

18. The computer program product of claim 17, wherein the operational parameter is selected from a group consisting of precision and recall.

19. The computer program product of claim 13, further comprising a step of selecting an appropriate script from a plurality of scripts and executing the selected script to identify concepts in the text.

20. The computer program product of claim 19, wherein the step of selecting an appropriate script from a plurality of scripts includes identifying a language in which the text is written, and selecting the script corresponding to the identified language.

21. The computer program product of claim 13, further comprising a step of using real-time feedback to modify the statistical information provided to one or more learning nodes of the set of learning nodes.

22. The computer program product of claim 21, wherein the real-time feedback comprises a response of a human agent to the relevance of the text to associated categories based upon the set of match scores.

23. The computer program product of claim 21, wherein the real-time feedback comprises a reply to the suggested action, the suggested action comprising a suggested response or a link to a web-resource.

24. The computer program product of claim 21, wherein the step of using real- time feedback to modify the statistical information comprises a step of modifying weights assigned to the statistical information.