Detection of communication topic change

• US 9,645,703 B2
• Filed: 05/14/2014
• Issued: 05/09/2017
• Est. Priority Date: 05/14/2014
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bhagwat, Anand B., Bohra, Amit P., Pal, Manojkumar, Shrivastava, Abhishek, Sahu, Swapnasarit
• Primary Examiner(s): SALOMON, PHENUEL S
• Application Number:
  US14/277,290
• Publication Number:
  US 20150332168A1
• Time in Patent Office: 1,091 Days
• Field of Search: None
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Claims

1. A method for determining a topic change of a communication, the method comprising:

determining, by a computer processor, a communication containing a set of dialog statements, wherein a first span of the communication includes one or more dialog statements of the set of dialog statements;

determining, by the computer processor, if the one or more dialog statements of the first span include one or more indicators of a topic change, wherein the one or more indicators are identified by at least one detector of a learning model;

responsive to determining the first span includes the one or more indicators of the topic change, generating, by the computer processor, a score for the one or more indicators, based on the learning model;

responsive to the score for the one or more indicators triggering a threshold condition, determining, by the computer processor, a topic change within the first span,

wherein the threshold condition determined during training of the learning model, includes;

determining, by the computer processor, a weighted value for the at least one detector, based on heuristics;

receiving input of labelled communication dialog statements, wherein the labelled communication dialog statements include one or more topic change indicators that are known, the one or more topic change indicators corresponding to the at least one detector;

adjusting, by the computer processor, the weighted value of the at least one detector in response to a delta between an output of scores of the at least one detector of the learning model and scores of the one or more topic change indicators that are known; and

determining, by the computer processor, the threshold condition in response to achieving an acceptable minimum for the delta between the output of the scores which are determined by the at least one detector of the learning model and the scores of the one or more topic change indicators that are known.

2. The method of claim 1, further comprising:

receiving, by a computer processor, the communication including the first span;

generating, by the computer processor, a second span based on adjusting boundaries of the first span by performing at least one of, adding to the first span, one or more dialog statements of the set of dialog statements not included in the first span, and removing one or more dialog statements from the first span;

determining, by the computer processor, a score for the first span and a score for the second span, wherein the score for the first span and the score for the second span is based on a topic of the first span and a topic of the second span, respectively;

responsive to the score of the second span being more favorable than the score of the first span, extracting, by the computer processor, one or more features from the one or more dialog statements of the second span not included in the first span; and

training, by the computer processor, the learning model to determine a topic change, based, at least in part, on including the one or more features from the one or more dialog statements of the second span, in at least one detector of the learning model.

3. The method of claim 2, wherein extracting the one or more features from the one or more dialog statements of the second span, includes classifying the one or more features to correspond with the at least one detector of the learning model.

4. The method of claim 1, wherein the threshold condition is determined during training of the learning model to determine the topic change within the first span of the communication.

5. The method of claim 1, wherein the one or more indicators of the topic change within the first span includes one or more of:

a particular key phrase, a pause of particular duration, a particular activity on a participant'"'"'s communication device, and a particular duration of the first span.

6. The method of claim 1, wherein the at least one detector of the learning model includes a keyword and key phrase based detector, and at least one of:

a time based detector, a personalized history based detector, a participant device activity based detector, and a text tiling based detector.

7. A computer program product for determining a topic change of a communication, the computer program product comprising:

a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising;

determining, a communication containing a set of dialog statements, wherein a first span of the communication includes one or more dialog statements of the set of dialog statements;

determining if the one or more dialog statements of the first span include one or more indicators of a topic change, wherein the one or more indicators are identified by at least one detector of a learning model;

responsive to determining the first span includes the one or more indicators of the topic change, generating a score for the one or more indicators, based on the learning model;

responsive to the score for the one or more indicators triggering a threshold condition, determining, by the computer processor, a topic change within the first span,

wherein the threshold condition determined during training of the learning model, includes;

determining, by the computer processor, a weighted value for the at least one detector, based on heuristics;

receiving input of labelled communication dialog statements, wherein the labelled communication dialog statements include one or more topic change indicators that are known, the one or more topic change indicators corresponding to the at least one detector;

adjusting, by the computer processor, the weighted value of the at least one detector in response to a delta between an output of scores of the at least one detector of the learning model and scores of the one or more topic change indicators that are known; and

determining, by the computer processor, the threshold condition in response to achieving an acceptable minimum for the delta between the output of the scores which are determined by the at least one detector of the learning model and the scores of the one or more topic change indicators that are known.

8. The computer program product of claim 7, further comprising:

receiving the communication including the first span;

generating a second span based on adjusting boundaries of the first span by performing at least one of, adding to the first span, one or more dialog statements of the set of dialog statements not included in the first span, and removing one or more dialog statements from the first span;

determining a score for the first span and a score for the second span, wherein the score for the first span and the score for the second span is based on a topic of the first span and a topic of the second span, respectively;

responsive to the score of the second span being more favorable than the score of the first span, extracting one or more features from the one or more dialog statements of the second span not included in the first span; and

training the learning model to determine a topic change, based, at least in part, on including the one or more features from the one or more dialog statements of the second span, in at least one detector of the learning model.

9. The computer program product of claim 8, wherein extracting the one or more features from the dialog statements of the second span, includes classifying the one or more features from the one or more dialog statements of the second span to correspond with the at least one detector of the learning model.

10. The computer program product of claim 7, wherein the threshold condition is determined during training of the learning model to determine the topic change within the communication.

11. The computer program product of claim 7, wherein the one or more indicators of the topic change within the first span includes one or more of:

a particular key phrase, a pause of particular duration, a particular activity on a participant'"'"'s communication device, and a particular duration of the first span.

12. The computer program product of claim 7, wherein the at least one detector of the learning model includes a keyword and key phrase based detector, and at least one of:

a time based detector, a personalized history based detector, a participant device activity based detector, and a text tiling based detector.

13. A computer system for determining a topic change of a communication, the computer system comprising:

one or more computer processors;

one or more computer readable storage media;

program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to determine a communication containing a set of dialog statements, wherein a first span of the communication includes one or more dialog statements of the set of dialog statements;

program instructions to determine if the one or more dialog statements of the first span include one or more indicators of a topic change, wherein the one or more indicators are identified by at least one detector of a learning model;

responsive to determining the first span includes the one or more indicators of the topic change, program instructions to generate a score for the one or more indicators, based on the learning model;

responsive to the score for the one or more indicators triggering a threshold condition, program instructions to determine a topic change within the first span,

wherein the threshold condition determined during training of the learning model, includes;

program instructions to determine a weighted value for the at least one detector, based on heuristics;

program instructions to receive input of labelled communication dialog statements, wherein the labelled communication dialog statements include one or more topic change indicators that are known, the one or more topic change indicators corresponding to the at least one detector;

program instructions to adjust the weighted value of the at least one detector in response to a delta between an output of scores of the at least one detector of the learning model and scores of the one or more topic change indicators that are known; and

program instructions to determine the threshold condition in response to achieving an acceptable minimum for the delta between the output of the scores which are determined by the at least one detector of the learning model and the scores of the one or more topic change indicators that are known.

14. The computer system of claim 13, further comprising:

program instructions to receive the communication including the first span;

program instructions to generate a second span based on adjusting boundaries of the first span by performing at least one of, adding to the first span, one or more dialog statements of the set of dialog statements not included in the first span, and removing one or more dialog statements from the first span;

program instructions to determine a score for the first span and a score for the second span, wherein the score for the first span and the score for the second span is based on a topic of the first span and a topic of the second span, respectively;

responsive to the score of the second span being more favorable than the score of the first span, program instructions to extract one or more features from the one or more dialog statements of the second span not included in the first span; and

program instructions to train the learning model to determine a topic change, based, at least in part, on including the one or more features from the one or more dialog statements of the second span, in at least one detector of the learning model.

15. The computer system of claim 14, further comprises:

determining the threshold condition during training of the learning model to identify a topic change within the first span of the communication; and

classifying the one or more features from the one or more dialog statements of the second span, to correspond with the at least one detector of the learning model.

16. The computer system of claim 13, wherein the one or more indicators of the topic change within the first span includes one or more of:

a particular key phrase, a pause of particular duration, a particular activity on a participant'"'"'s communication device, and a particular duration of the first span.

17. The computer system of claim 13, wherein the at least one detector of the learning model includes a keyword and key phrase based detector, and at least one of:

a time based detector, a personalized history based detector, a participant device activity based detector, and a text tiling based detector.