CRF Tagging Model Training Using Implicit Labels
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Solution Overview
Problem
Conditional random field (CRF) models require large manually annotated training sets for accurate operation, which can be impractical, and existing training approaches have shortcomings in utilizing implicit labels effectively.
Innovation Solution
A training module that combines explicit and implicit labels, treating implicit labels as hard or soft evidence to generate a CRF tagging model, using an expectation-maximization algorithm and soft evidence feature functions to optimize the training objective, allowing for unsupervised or partially unsupervised generation of implicit labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large manually annotated training set is used to train CRF model, then the model accuracy is improved, but the annotation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary unsupervised label generation before final model training, creating initial labels that are then refined through supervised correction. This preliminary action reduces the amount of manual annotation needed while maintaining model accuracy.
Solution Approach 2:
Implicit labels generated by the unsupervised system serve as an intermediary between raw data and final model training. These intermediate labels guide the training process and reduce dependency on extensive manual annotations.
2Productivity
If implicit labels are used to train CRF model, then the training efficiency is improved, but the label accuracy may be reduced compared to manual annotation
Solution Approach 1:
The system merges implicit labels generated by the unsupervised system with explicit manual annotations. This combination allows the model to benefit from both the efficiency of automated labeling and the accuracy of human annotation, resolving the trade-off between training efficiency and label accuracy.
Solution Approach 2:
The system applies different labeling qualities to different data portions: implicit labels are used for portions where high accuracy is less critical, while explicit manual annotations are applied to portions requiring higher precision. This local differentiation optimizes both efficiency and accuracy.
3Reliability
If soft evidence approach is used to treat implicit labels, then the model robustness is improved, but the training complexity increases
Solution Approach 1:
The system introduces weighting parameters that control the influence of implicit labels during training. By adjusting these parameters, the system can balance between using soft evidence for robustness and managing training complexity, allowing flexible control over the trade-off.
Data Source
AI summary
A training module is described for training a conditional random field (CRF) tagging model. The training module trains the tagging model based on an explicitly-labeled training set and an implicitly-labeled training set. The explicitly-labeled training set includes explicit labels that are manually selected via human annotation, while the implicitly-labeled training set includes implicit labels that are generated in an unsupervised manner. In one approach, the training module can train the tagging model by treating the implicit labels as non-binding evidence that has a bearing on values of hidden state sequence variables. In another approach, the training module can treat the implicit labels as binding or hard evidence. A labeling system is also described for providing the implicit labels.