Categorizer Training via Search-and-Confirm Mechanism
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Solution Overview
Problem
Conventional methods for training categorizers are inefficient and costly, often relying on labeled training sets that are difficult and expensive to obtain, and may not provide accurate results due to reliance on untrained customer support representatives or experts, and do not account for situations where the correct answer cannot be objectively ascertained.
Innovation Solution
A search-and-confirm mechanism that allows for the concurrent development of training cases from unlabeled and labeled data sets, using a search engine and confirmation module to create positive and negative training sets, enabling users to focus on a single category at a time and infer hierarchical relationships, thereby simplifying the training process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional methods use labeled training sets obtained from surveys or third parties, then training data can be acquired, but the process is expensive in terms of time and effort
Solution Approach 1:
The system allows customer support representatives to self-generate training data by selecting categorizations during normal call handling. The training set is created as a byproduct of their日常工作, eliminating the need for separate surveys or external data collection processes. This transforms their routine work into a dual-purpose activity that both resolves customer issues and builds training data.
Solution Approach 2:
The system pre-presents relevant categories to customer support representatives during call handling, based on the call context and customer statements. This preliminary presentation of likely categories guides representatives to make accurate selections without requiring them to have expert-level knowledge of all possible categories, thereby reducing training time and effort.
2Quantity of substance
If customer support representatives are used to provide categorizations, then training data can be obtained, but accuracy may be insufficient due to lack of proper training
Solution Approach 1:
The system introduces an intermediary layer between the customer support representative and the final categorization. The representative's selection is processed through algorithms that validate, correct, or supplement the categorization based on call analysis, ensuring higher accuracy even when representatives lack expert knowledge.
Solution Approach 2:
The system implements feedback mechanisms where categorization selections are reviewed and validated. Incorrect or uncertain selections trigger feedback to the representative for correction, and the system learns from these interactions to improve future categorization suggestions, thereby increasing overall accuracy over time.
3Measurement precision
If experts are used to provide correct answers for training cases, then accuracy improves, but the process becomes relatively expensive
Solution Approach 1:
The system uses a partial expert approach where only certain cases requiring expert judgment are reviewed by experts, while other cases are handled automatically or by trained representatives. This selective application of expert review reduces costs and time while maintaining accuracy for critical cases.
Solution Approach 2:
The system enables representatives to independently handle most categorization tasks through the guided interface and automated suggestions, reducing reliance on expensive expert intervention. Experts are only involved when necessary, making the process more cost-effective while maintaining sufficient accuracy.
4Productivity
If conventional training requires separate acquisition and training stages, then the process is systematic, but the process becomes complex and time-consuming
Solution Approach 1:
The system merges the data acquisition phase and the training phase into a single integrated process. Customer support representatives simultaneously perform their primary function of handling calls and contribute to building the training set, eliminating the need for separate sequential stages and reducing overall complexity.
Solution Approach 2:
The categorization selection interface serves multiple functions: it resolves the current customer call, generates training data, and trains the categorization system. This multi-functionality eliminates the need for separate processes and reduces overall system complexity while improving productivity.
Data Source
AI summary
A method and system of providing training information for training a categorizer includes receiving a query relating to at least one category and identifying at least one case within a data set that matches the query. The method and system receives one of a first indication that the identified at least one case belongs to the category, and a second indication that the identified at least one case does not belong to the category. Training information is modified based on receiving one of the first indication and second indication.