SYSTEM AND METHOD FOR SEMANTICALLY EXPLORING CONCEPTS

• US 20160012818A1
• Filed: 07/09/2014
• Published: 01/14/2016
• Est. Priority Date: 07/09/2014
• Status: Active Grant

• Current Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Original Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Faizakof, Avraham, Lev-Tov, Amir, Konig, Yochai, Lev, Yoni
• Granted Patent: US 9,842,586 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/268 Morphological analysis
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 2015/0631 Creating reference templates; Clustering
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command

Abstract

A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.

Claims

1. A method for detecting and categorizing topics in a plurality of interactions, the method comprising:

extracting, by a processor, a plurality of fragments from the plurality of interactions;

filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments;

clustering, by the processor, the filtered fragments into a plurality of base clusters; and

clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.

2. The method of claim 1, wherein the extracting the plurality of fragments from the plurality of interactions comprises:

receiving, by the processor, text corresponding to the plurality of interactions;

tagging, by the processor, portions of the text based on parts of speech; and

extracting, by the processor, fragments from the text in accordance with one or more extraction rules.

3. The method of claim 2, wherein the text corresponding to the plurality of interactions comprises an output of an automatic speech recognition engine, the output being generated by processing at least one of the plurality of interactions through the automatic speech recognition engine.

4. The method of claim 2, wherein the one or more extraction rules comprise a part of speech sequence.

5. The method of claim 2, wherein the one or more extraction rules are automatically generated by the processor based on a plurality of manually extracted fragments.

6. The method of claim 1, further comprising labeling, by the processor, a base cluster of the plurality of base clusters, the labeling comprising:

extracting, by the processor, a plurality of noun phrases from the base cluster;

computing, by the processor, a distribution of probabilities of stems of the noun phrases; and

identifying, by the processor, a label noun phrase of the noun phrases, the label noun phrase having a highest probability based on the stem distribution.

7. The method of claim 1, wherein the clustering the plurality of base clusters into the plurality of hyper clusters comprises:

computing, by the processor, a plurality of semantic distances between pairs of the plurality of base clusters; and

clustering, by the processor, the base clusters into the hyper clusters in accordance with the semantic distances.

8. The method of claim 7, wherein the plurality of semantic distances are computed based on semantic similarities of the pairs of base clusters and co-occurrence of fragments in the pairs of base clusters.

9. The method of claim 1, further comprising:

generating, by the processor, a visualization of the plurality of topics as organized into a hierarchy based on the plurality of hyper clusters, at least one of the hyper clusters comprising a plurality of corresponding base clusters, each of the base clusters comprising a corresponding plurality of fragments.

10. A system comprising:

a processor; and

a memory, wherein the memory has stored thereon instructions that, when executed by the processor, cause the processor to;

receive a plurality of interactions;

extract a plurality of fragments from the plurality of interactions;

filter the plurality of fragments to generate a filtered plurality of fragments;

cluster the filtered fragments into a plurality of base clusters; and

cluster the plurality of base clusters into a plurality of hyper clusters.

11. The system of claim 10, wherein the instructions that cause the processor to extract the plurality of fragments from the plurality of interactions comprise instructions that, when executed by the processor, cause the processor to:

receive text corresponding to the plurality of interactions;

tag portions of the text based on parts of speech; and

extract fragments from the text in accordance with one or more extraction rules.

12. The system of claim 11, wherein the text corresponding to the plurality of interactions comprises an output of an automatic speech recognition engine, the output being generated by processing at least one of the plurality of interactions through the automatic speech recognition engine.

13. The system of claim 11, wherein the one or more extraction rules comprise a part of speech sequence.

14. The system of claim 11, wherein the memory further has stored thereon instructions that, when executed by the processor, cause the processor to generate the one or more extraction rules based on a plurality of manually extracted fragments.

15. The system of claim 10, wherein the memory further has stored thereon instructions that, when executed by the processor, cause the processor to label a base cluster of the plurality of base clusters by:

extracting a plurality of noun phrases from the base cluster;

computing a distribution of probabilities of stems of the noun phrases; and

identifying a label noun phrase of the noun phrases, the label noun phrase having a highest probability based on the stem distribution.

16. The system of claim 10, wherein the instructions that cause the processor to cluster the plurality of base clusters into the plurality of hyper clusters comprise instructions that, when executed by the processor, cause the processor to:

compute a plurality of semantic distances between pairs of the plurality of base clusters; and

cluster the base clusters in into the hyper clusters in accordance with the semantic distances.

17. The system of claim 16, wherein the instructions that cause the processor to compute the plurality of semantic distances between the pairs of the base clusters comprise instructions to compute a semantic distance of the semantic distances based on semantic similarities between the pairs of the base clusters and co-occurrence of fragments in the pairs of the base clusters.

18. The system of claim 10, wherein the memory further has stored thereon instructions that, when executed by the processor, cause the processor to generate a visualization of a plurality of topics as organized into a hierarchy based on the plurality of hyper clusters, at least one of the hyper clusters comprising a plurality of corresponding base clusters, each of the base clusters comprising a corresponding plurality of fragments.