Document Search Performance Degradation Detection via Label Variation
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Solution Overview
Problem
The performance of document search systems degrades due to changes in database information, causing relevant documents to be incorrectly ranked, as new data is added, leading to variations in search results even with the same query.
Innovation Solution
A method and system that utilize a language model generated by machine learning to evaluate document search performance by calculating relevance degrees between document data and search queries, with first and second labels attached to data, detecting performance degradation when variation values exceed specific thresholds after updating the language model with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new document data is added to the database, then the database is updated with latest information, but the search performance degrades due to changing information tendencies
Solution Approach 1:
The system performs preliminary actions by attaching first labels (specific concepts) and second labels (superordinate concepts) to document data before adding it to the database. This pre-labeling enables subsequent performance evaluation without requiring manual intervention after data addition, allowing the system to proactively detect and respond to performance degradation.
Solution Approach 2:
The system implements feedback mechanisms by calculating statistical values based on search results using first and second labels, comparing variations in ranking statistics, and detecting performance degradation when variations exceed thresholds. This feedback loop enables the system to monitor search quality and trigger updates to the language model when necessary.
2Adaptability or versatility
If the language model is updated with new data, then the model adapts to current information, but search result rankings become less accurate
Solution Approach 1:
The system attaches multiple labels (first labels for specific concepts and second labels for superordinate concepts) to document data before adding it to the database. This preliminary labeling structure enables the system to evaluate search performance across different concept levels and detect when language model updates cause accuracy degradation.
Solution Approach 2:
The system monitors changes in statistical parameters (ranking statistics) before and after language model updates. By calculating variations in these parameters and comparing them against thresholds, the system can detect when model updates negatively impact relevance degree accuracy and trigger corrective actions.
3Device complexity
If document search uses traditional keyword matching, then the system is simple to implement, but it cannot handle changes in information tendency over time
Solution Approach 1:
The system uses feedback mechanisms to monitor search performance by calculating statistical values from search results and detecting variations in ranking statistics. This feedback enables the system to automatically detect when traditional keyword matching becomes inadequate due to changing information tendencies and triggers language model updates.
Solution Approach 2:
The system replaces traditional mechanical keyword matching with a language model-based semantic search system. The language model captures semantic relationships and contextual meanings, enabling the system to handle changes in information tendency while maintaining relative simplicity through automated evaluation and update mechanisms.
Data Source
AI summary
Degradation in performance of document search is automatically detected. When document data is added to a database, a learning unit updates a language model by machine learning. A performance evaluation unit respectively calculates a first statistical value and a second statistical value related to ranking of each of a plurality of specific document data from a result of the document search using each of a plurality of first labels and second labels attached to a plurality of specific document data as a search query. The performance evaluation unit detects the degradation in performance of the document search when a variation value of the first statistical value due to update of a language model accompanying addition of at least one piece of document data is larger than a first threshold, and when a variation value of the second statistical value due to update is larger than a second threshold.