Label Assignment Model Deviation Adjustment
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Solution Overview
Problem
Existing label assignment models for document search do not adequately consider the deviation in word number when multiple words share common feature elements, which can affect the accuracy of the classifier's characteristics.
Innovation Solution
A label assignment model generation device and method that extracts feature amounts from documents, generates a machine learning model based on appearance frequency, and adjusts the influence of common feature amounts by considering the deviation in word number, using a deviation adjustment unit to refine the label assignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used to assign labels to words for improving search accuracy, then search accuracy is improved, but the model requires learning data of good quality which increases complexity in data preparation
Solution Approach 1:
The system performs preliminary extraction of feature amounts from documents before generating the label assignment model. By pre-processing and organizing feature data (such as word frequencies, co-occurrence patterns, and document metadata) into structured learning data, the system reduces the complexity of data preparation while ensuring high-quality input for the machine learning model, thereby maintaining search accuracy without increasing operational complexity
2Measurement precision
If feature elements are extracted from discrimination candidates for label assignment, then label assignment accuracy is improved, but the system does not consider deviation of word number which affects classifier characteristics
Solution Approach 1:
The system introduces word number deviation as a new parameter to be extracted and considered in the feature amount analysis. By calculating the deviation of word numbers across multiple documents and incorporating this into the feature vector, the system adjusts the learning data to reflect variations in document structure. This parameter change enables the classifier to account for word number deviations, improving both label assignment accuracy and the reliability of classifier characteristics across different document types
Data Source
AI summary
A label assignment model generation device extracts a plurality of feature amounts for a word from a document as an extraction source, and generates, based on an appearance frequency of each of the extracted feature amounts, a label assignment model that is a machine learning model and assigns a label to a word included in a document as an assignment target. The label assignment model generation device adjusts a degree of influence of the feature amount on the label assignment model based on a deviation of the appearance frequency of each of the plurality of feature amounts. The label assignment model generation device extracts a plurality of feature amounts for a word from a remaining document excluding a predetermined document from a plurality of documents as extraction sources, and generates the label assignment model based on the appearance frequency of the extracted feature amounts.