Automated Call Reason Extraction from Service Transcripts
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Solution Overview
Problem
Conventional methods for extracting call reasons from customer service transcripts are limited by analyst knowledge, unable to discover emerging trends, and struggle with capturing complex or nuanced events, particularly in noisy and non-standard conversational English.
Innovation Solution
A system that uses natural language processing techniques to extract events, cluster them based on distributional information, and label clusters to generate fine-grained call reasons, capable of handling complex events and new vocabulary without relying on static resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional NLP systems are used to extract call reasons from transcripts, then existing methods can process transcripts, but they are limited by analyst knowledge and cannot discover emerging trends
Solution Approach 1:
The system performs self-service by automatically discovering call reason categories through unsupervised clustering of events extracted from transcripts. The cluster generation system identifies patterns and creates categories without requiring pre-defined taxonomies or analyst expertise, enabling the system to adapt to emerging trends autonomously
Solution Approach 2:
The system changes the approach from using fixed, pre-defined call reason categories to dynamically generating categories based on the actual data patterns. By clustering events based on their semantic features and relationships, the system adapts its classification framework to match emerging trends and nuanced events that conventional methods would miss
2Measurement precision
If conventional methods are used, then processing is simpler, but complex or nuanced events cannot be captured accurately
Solution Approach 1:
The system segments the complex task of call reason extraction into distinct functional components: event extraction, cluster generation, and labeling. This segmentation allows each component to focus on specific aspects of the data, improving precision for capturing nuanced events while managing overall system complexity through modular architecture
Solution Approach 2:
The cluster generation system acts as an intermediary between raw event data and final call reason classifications. It processes extracted events through intermediate clustering and labeling steps, enabling accurate capture of complex and nuanced events by finding patterns in the data rather than relying on pre-defined categories
3Productivity
If manual analysis is performed to ensure accuracy, then nuanced events can be captured, but time consumption increases significantly
Solution Approach 1:
The system eliminates the need for manual analysis by performing automated event extraction, clustering, and labeling of call reasons. The unsupervised learning algorithms process transcripts independently, identifying patterns and generating categories without human intervention, thereby dramatically increasing productivity while reducing time loss
Solution Approach 2:
The system replaces the mechanical process of manual transcript review with automated NLP-based processing. By using natural language processing techniques for event extraction and clustering algorithms for categorization, the system achieves high-speed processing that substitutes for time-consuming manual analysis while maintaining or improving accuracy
4Adaptability or versatility
If static resources and predefined categories are used, then processing is faster, but the system cannot handle new vocabulary or emerging trends
Solution Approach 1:
The system transitions from static, predefined categories to dynamic, data-driven category generation. The cluster generation system continuously learns from the data and creates new categories as needed, enabling the system to adapt to new vocabulary and emerging trends in real-time without requiring updates to a fixed taxonomy
Solution Approach 2:
The system performs self-learning by automatically discovering patterns in the data and generating appropriate categories without external intervention. This self-service capability allows the system to handle new vocabulary and emerging trends autonomously, maintaining fast processing speeds while improving adaptability to changing linguistic patterns
Data Source
AI summary
Embodiments disclosed are directed to a computing system that performs steps to automatically generate fine-grained call reasons from customer service call transcripts. The computing system extracts, using a natural language processing (NLP) technique, a set of events from a set of text strings of speaker turns. The computing system then identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events. Subsequently, the computing system assigns each event in the set of events to a respective labeled cluster of events in the set of labeled clusters of events.