Hierarchical State Machine Structure Learning for Label Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hierarchical state machines for text or token sequence labeling often suffer from oversimplicity or excessive complexity, failing to capture inherent dependencies in data, and require manual coding or default structures that are computationally costly without adequate benefits.
Innovation Solution
The approach involves learning the structure of a hierarchical state machine from labeled training data using statistical distributions of label sequences, employing frequent-length and frequent-sequence encoding methods to generate non-cyclic directed chains of states, and weighting transitions based on these distributions, thereby encoding important dependencies without unnecessary complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the hierarchical state machine structure is hand-coded, then the model can capture data dependencies and constraints, but the development time and complexity increase significantly
Solution Approach 1:
The system automatically learns the hierarchical state machine structure from labeled training data through statistical analysis of label sequences. The model self-configures its own architecture by identifying frequent label sequences and their statistical distributions, eliminating the need for manual structure design while capturing data dependencies inherent in the training data
Solution Approach 2:
The system performs preliminary statistical analysis on training data to determine the optimal hierarchical state machine structure before actual use. By pre-computing frequent label sequences and their distributions from training data, the system prepares the structure in advance, avoiding complex manual design while ensuring the structure captures relevant data patterns
2Extent of automation
If default sub-concept state machines are used, then the construction process is automated, but the models suffer from oversimplicity and fail to capture data dependencies
Solution Approach 1:
The system applies different structural patterns to different parts of the hierarchical state machine based on the statistical properties of local label sequences. By analyzing frequent label sequences and their distributions locally, the system customizes the structure for each sub-concept, ensuring that each part captures its specific data dependencies rather than applying a uniform default structure
Solution Approach 2:
The system dynamically adjusts the hierarchical state machine structure by changing parameters such as the number of states, transition probabilities, and sequence lengths based on statistical analysis of training data. This allows the model to adapt its complexity and structure to match the actual data patterns, avoiding oversimplification while maintaining automation
3Reliability
If complex default sub-concept state machines are used, then more data dependencies are captured, but the computational cost increases excessively
Solution Approach 1:
The system uses only the necessary portion of the hierarchical state machine structure that is supported by statistical evidence from training data. By identifying and modeling only frequent label sequences and their distributions, the system avoids the excessive complexity of comprehensive default structures while still capturing the most important data dependencies
Solution Approach 2:
The system optimizes computational efficiency by adjusting parameters such as the maximum sequence length, the number of frequent sequences to track, and the granularity of statistical analysis. These parameter changes allow the model to capture essential data dependencies while controlling computational cost through evidence-based structural simplification
Data Source
AI summary
A hierarchical extraction model for a label hierarchy may be implemented by a weighted hierarchical state machine whose structure and/or weights are determined in part from a statistical distribution of label sequences as determined from training data. In accordance with various embodiments, the hierarchical state machine includes one or more non-cyclic directed chains of states representing at least a subset of the label sequences, and transitions weighted based at least in part on the statistical distribution.