Determination of conversation threads in a message channel based on conversational flow and semantic similarity of messages
AssigneeInternational Business Machines Corporation
InventorsMing Tan, Haoyu Wang, Dakuo Wang, Chuang Gan
Life of this patent
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Abstract
A deep learning module classifies messages received from a plurality of entities into one or more conversation threads. In response to receiving a subsequent message, the deep learning module determines which of the one or more conversation threads and a new conversation thread is contextually a best fit for the subsequent message. The subsequent message is added to the determined conversation thread.
Claims14
A method, comprising:
- classifying, by a deep learning module implemented in a computational device, messages received from a plurality of entities into one or more conversation threads;
- determining a flow fit measure for a subsequent message for potential addition to each of the one or more conversation threads;
- determining a semantic match measure for the subsequent message for the potential addition to each of the one or more conversation threads;
- and
- in response to the determining of the flow fit measure and the semantic match measure, classifying the subsequent message into a conversation thread that has a best flow fit and semantic match combination measure in comparison to other conversation threads of the one or more conversation threads, wherein the flow fit measure represents a fluency of conversation in the conversation thread if the subsequent message is added to the conversation thread.
A system, comprising:
- a memory;
- and
- a processor coupled to the memory, wherein the processor performs operations, the operations comprising:
- classifying, by a deep learning module implemented in a computational device, messages received from a plurality of entities into one or more conversation threads;
- determining a flow fit measure for a subsequent message for potential addition to each of the one or more conversation threads;
- determining a semantic match measure for the subsequent message for the potential addition to each of the one or more conversation threads;
- and
- in response to the determining of the flow fit measure and the semantic match measure, classifying the subsequent message into a conversation thread that has a best flow fit and semantic match combination measure in comparison to other conversation threads of the one or more conversation threads, wherein the flow fit measure represents a fluency of conversation in the conversation thread if the subsequent message is added to the conversation thread.
A computer program product, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform operations on a processor, the operations comprising:
- classifying, by a deep learning module implemented in a computational device, messages received from a plurality of entities into one or more conversation threads;
- determining a flow fit measure for a subsequent message for potential addition to each of the one or more conversation threads;
- determining a semantic match measure for the subsequent message for the potential addition to each of the one or more conversation threads;
- and
- in response to the determining of the flow fit measure and the semantic match measure, classifying the subsequent message into a conversation thread that has a best flow fit and semantic match combination measure in comparison to other conversation threads of the one or more conversation threads, wherein the flow fit measure represents a fluency of conversation in the conversation thread if the subsequent message is added to the conversation thread.
Dependencies are inferred from the claim text.