Extending system entities for conversational system
AssigneeInternational Business Machines Corporation
InventorsLi Jun Mei, Qi Cheng Li, Rangachari Anand, Ajay Gupta
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Abstract
A computer-implemented method is provided for processing a set D of conversation logs. The method includes learning, by a processor device, a set R of entity relation mining rules from a set K of known entity utterances uttered by known entities. The method further includes automatically recommending, by the processor device, extended utterances of the known entities from unrecognized ones of the known entities based on the set R.
Claims20
A computer-implemented method for processing a set D of conversation logs, comprising:
- learning, by a processor device, a set R of entity relation mining rules from a set K of known entity utterances uttered by known entities;
- and
- automatically recommending, by the processor device, extended utterances of the known entities from unrecognized ones of the known entities based on the set R;
- wherein said learning step comprises:
- replacing, in the set D of conversation logs, any of the known entity utterances from the set K that appear in the set D, by a respective representative utterance having a same meaning to form a set D1 of unified conversation logs;
- finding a set CS of chat log segments in the set D1 which include at least two of the same utterances in the set D1;
- generating the set R of entity relation mining rules based on the set CS;
- randomly selecting a subset K1 from the set K;
- for each of the respective representative utterance in the set D1 that also occurs in the subset K1, replacing the respective representative utterance with a corresponding original utterance from the set D to form a list D2 of conversation logs;
- removing the subset K1 from the set K;
- and
- for each individual rule r in the set R, applying the individual rule r on the list D2, checking a success rate of identifying utterance relations in the subset K1, and removing the individual rule r from the set R if the success rate is below a rule success rate threshold.
A computer program product for processing a set D of conversation logs, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
- learning, by a processor device, a set R of entity relation mining rules from a set K of known entity utterances uttered by known entities;
- and
- automatically recommending, by the processor device, extended utterances of the known entities from unrecognized ones of the known entities based on the set R;
- wherein said learning step comprises:
- replacing, in the set D of conversation logs, any of the known entity utterances from the set K that appear in the set D, by a respective representative utterance having a same meaning to form a set D1 of unified conversation logs;
- finding a set CS of chat log segments in the set D1 which include at least two of the same utterances in the set D1;
- generating the set R of entity relation mining rules based on the set CS;
- randomly selecting a subset K1 from the set K;
- for each of the respective representative utterance in the set D1 that also occurs in the subset K1, replacing the respective representative utterance with a corresponding original utterance from the set D to form a list D2 of conversation logs;
- removing the subset K1 from the set K;
- and
- for each individual rule r in the set R, applying the individual rule r on the list D2, checking a success rate of identifying utterance relations in the subset K1, and removing the individual rule r from the set R if the success rate is below a rule success rate threshold.
A computer processing system for processing a set D of conversation logs, comprising:
- a memory for storing program code;
- and
- a processor device for running the program code to
- learn a set R of entity relation mining rules from a set K of known entity utterances uttered by known entities;
- and
- automatically recommend extended utterances of the known entities from unrecognized ones of the known entities based on the set R;
- wherein the set R is learned by:
- replacing, in the set D of conversation logs, any of the known entity utterances from the set K that appear in the set D, by a respective representative utterance having a same meaning to form a set D1 of unified conversation logs;
- finding a set CS of chat log segments in the set D1 which include at least two of the same utterances in the set D1;
- generating the set R of entity relation mining rules based on the set CS;
- randomly selecting a subset K1 from the set K;
- for each of the respective representative utterance in the set D1 that also occurs in the subset K1, replacing the respective representative utterance with a corresponding original utterance from the set D to form a list D2 of conversation logs;
- removing the subset K1 from the set K;
- and
- for each individual rule r in the set R, applying the individual rule r on the list D2, checking a success rate of identifying utterance relations in the subset K1, and removing the individual rule r from the set R if the success rate is below a rule success rate threshold.
Dependencies are inferred from the claim text.