Hierarchical Multi-Label Text Classification for Document Assignment
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Solution Overview
Problem
Current systems for assigning documents to appropriate personnel in service-oriented organizations often result in iterative processes that can lead to negative customer experiences due to inefficiencies and delays, affecting overall communication and customer satisfaction.
Innovation Solution
A system utilizing hierarchical multi-label text classification (HMTC) and correlator models to predict the most suitable assignee by generating vectors based on document content and historical performance data, enabling efficient and effective assignment of tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative assignment process is used with tiered groups, then documents can be resolved by appropriate expertise levels, but communication efficiency deteriorates and customer satisfaction worsens
Solution Approach 1:
The system performs preliminary classification of documents into hierarchical categories before assignment, and pre-evaluates assignee performance metrics across different label categories. This advance preparation enables direct assignment to the most suitable assignee without iterative transfers, resolving documents in one attempt while maintaining expertise-based routing.
Solution Approach 2:
The system continuously collects performance feedback from resolved documents and uses it to update assignee performance data. This feedback loop enables the system to learn from past resolutions and improve future assignment accuracy, ensuring documents are routed to the most effective assignees based on proven performance rather than static tiered structures.
2Reliability
If multiple iterative assignments are performed across tiers, then complex issues can be escalated to appropriate expertise, but time consumption increases and customer experience deteriorates
Solution Approach 1:
The system performs preliminary classification of documents into hierarchical categories before assignment, and pre-evaluates assignee performance metrics across different label categories. This advance preparation enables direct assignment to the most suitable assignee without iterative transfers, resolving documents in one attempt while maintaining expertise-based routing.
Solution Approach 2:
The system changes the assignment parameter from static tiered group assignment to dynamic individual assignee selection based on performance metrics. By evaluating multiple parameters including assignee performance history, document complexity, and label hierarchy, the system optimizes assignment to minimize time while ensuring appropriate expertise is applied.
3Productivity
If simple tiered group assignment is used, then assignment process is fast and simple, but assignment accuracy deteriorates and customer satisfaction worsens
Solution Approach 1:
The system continuously collects performance feedback from resolved documents and uses it to update assignee performance data. This feedback loop enables the system to learn from past resolutions and improve future assignment accuracy, ensuring documents are routed to the most effective assignees based on proven performance rather than static tiered structures.
Solution Approach 2:
The system changes the assignment parameter from static tiered group assignment to dynamic individual assignee selection based on performance metrics. By evaluating multiple parameters including assignee performance history, document complexity, and label hierarchy, the system optimizes assignment to minimize time while ensuring appropriate expertise is applied.
4Measurement precision
If hierarchical multi-label classification and performance feedback system are implemented, then assignment accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the assignment process into distinct functional modules: document classification into hierarchical labels, performance data collection from resolved documents, vector generation from performance data, similarity comparison between document vectors and assignee vectors, and assignee selection. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system introduces performance feedback data as an intermediary element that bridges document characteristics and assignee selection. By using performance feedback as a mediator, the system translates complex assignment criteria into quantifiable metrics that can be processed through vector similarity comparison, simplifying the decision-making process while maintaining high accuracy.
Data Source
AI summary
Logic may provide a hierarchical label structure for a document with a predicted set of hierarchical labels. Logic may provide a historical label performance database comprising performance data associated with each assignee in a complete set of assignees for each label in a complete set of the hierarchical labels. Logic may generate a first vector for the hierarchical label structure for the document. Logic may generate a second vector for the assignees in an identified set of assignees, comprising each hierarchical label in the predicted set of hierarchical labels for the document, the identified set comprising one or more of the assignees in the complete set of assignees. Logic may perform a similarity search to identify a predicted assignee from the identified set of assignees and logic may predict a selected assignee of the identified set of assignees to associate with the document via the similarity search.