Document Similarity Reordering for ML Annotation Efficiency
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Solution Overview
Problem
Manual annotation of documents for machine learning models is time-consuming and prone to errors, leading to poor model performance and bias, as users must review and correct auto-annotation tools page by page, which is inefficient and labor-intensive.
Innovation Solution
A method to reorder documents based on their similarity values, where documents with fewer similar page pairings are presented last to the auto-annotation tool, reducing manual correction efforts by prioritizing documents with more similar pages for initial annotation, thereby improving annotation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If documents are annotated in random or default order, then annotation process is simple to implement, but manual correction time and effort increase significantly
Solution Approach 1:
The system performs preliminary actions by calculating document similarity values and determining the optimal presentation order before the annotation process begins. Documents are pre-processed to compute similarity metrics, and the annotation tool is configured with this predetermined order, eliminating the need for manual reordering during annotation and reducing overall annotation time.
Solution Approach 2:
The system changes the parameter of document presentation order based on calculated similarity values. By transforming the static random or default order into a dynamic similarity-based order, the system optimizes the annotation process. The similarity value parameter is computed for each document pair, and this parameter drives the reordering of documents in the presentation sequence.
2Measurement precision
If auto-annotation tools are used without reordering, then annotation process is faster to start, but annotation accuracy decreases due to bias from similar documents
Solution Approach 1:
The system changes the presentation order parameter of documents based on similarity calculations. By computing similarity values between document pairs and reordering documents in descending order of similarity, the system ensures that most similar documents are annotated first. This parameter change improves annotation accuracy by reducing bias while maintaining efficiency through automated ordering.
Solution Approach 2:
The system implements feedback by using the calculated similarity values to continuously optimize the document presentation order. The similarity metrics serve as feedback that informs the reordering process, allowing the annotation tool to adapt its processing sequence based on document characteristics. This feedback mechanism ensures both accuracy and efficiency are optimized.
3Reliability
If documents with high similarity are presented first, then annotation accuracy improves by reducing bias, but calculation and ordering complexity increases
Solution Approach 1:
The system changes the document presentation order parameter based on similarity calculations to improve model training reliability. By computing similarity values and reordering documents accordingly, the system reduces bias in annotation. The parameter change from default order to similarity-based order ensures that documents with higher similarity are processed first, enhancing training reliability while the automated process manages the calculation complexity.
Data Source
AI summary
A method for one or more processors to select a plurality of documents. The one or more processors calculate a similarity between pairs of pages of respective documents of the plurality of documents. The one or more processors determine a document similarity value of the respective documents of the plurality of documents, based on a quantity of the pairs of pages of the respective document with a similarity calculated to be less than a predetermined threshold, and the one or more processors present the plurality of documents in a descending order, based on the document similarity value of the respective documents of the plurality of documents.