Chatbot Intent Clustering for Trending Topic Adaptation
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Solution Overview
Problem
Existing conversational systems, such as chatbots, face challenges in automatically identifying and updating intents to effectively respond to user queries, particularly when external events or trending topics emerge, leading to a delay in incorporating relevant information and user experiences.
Innovation Solution
A system and method that analyze chatbot logs and social media content to cluster potential intents and detect trending topics, computing semantic similarities to correlate intents with trending topics, thereby automatically triggering updates to chatbot training data to enhance its responsiveness and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If chatbots manually update intents based on user queries, then the chatbot can respond to user questions, but the process is time-consuming and fails to capture trending topics timely
Solution Approach 1:
The system automatically clusters chatbot logs into intent groups and detects trending topics without human intervention. The automated correlation between intent clusters and trending topics enables self-service intent updates, eliminating manual processing delays and capturing trending topics in real-time.
Solution Approach 2:
The system continuously monitors chatbot interaction logs and social media content, using feedback loops to detect trending topics and update intents dynamically. This feedback mechanism ensures the chatbot adapts to changing user inquiries and trending topics without time delay.
2Adaptability or versatility
If the chatbot analyzes all chatbot logs to identify intents, then comprehensive intent coverage is achieved, but the processing complexity and time increase
Solution Approach 1:
The system segments chatbot logs into distinct intent clusters using clustering algorithms, organizing vast amounts of data into manageable groups based on semantic similarity. This segmentation reduces processing complexity while maintaining comprehensive intent coverage by focusing on pattern recognition within clusters.
Solution Approach 2:
The system changes the parameter of log analysis from individual query processing to cluster-level analysis. By aggregating logs into intent clusters and analyzing them as unified entities, the system reduces processing complexity while maintaining comprehensive intent coverage through cluster representation.
3Productivity
If the chatbot updates intents frequently to capture trending topics, then responsiveness improves, but the training data management complexity increases
Solution Approach 1:
The system performs preliminary clustering of chatbot logs into intent clusters before correlation with trending topics. This preliminary organization of data reduces the complexity of subsequent updates, allowing frequent intent modifications without overwhelming training data management complexity.
Solution Approach 2:
The system extracts and isolates trending topic information from social media content, separating it from the main chatbot training data. This extraction allows frequent updates based on trending topics without proportionally increasing overall training data management complexity, as only the relevant trending portions need to be integrated.
Data Source
AI summary
Chatbot logs can be received. The chatbot logs can be clustered or classified into clusters of potential intents. Trending topics can be detected by analyzing at least social media content. Semantic similarities between the potential intents and the trending topics can be computed. Based on the computed semantic similarities, at least one of the potential intents to at least one of the trending topics can be correlated. Training of the chatbot using the chatbot logs associated with the correlated potential intent can be triggered.