Publications / Domestic Conferences & Theses
Rule-Augmented Neural Network-Based Dependency Parsing
Abstract
Added a transformer layer to existing graph-based dependency parsing models to incorporate additional feature embeddings and address gradient vanishing issues. Proposed a method that controls the final classification probability values according to predefined rules.
Key Contribution
Transformer-augmented dependency parser with rule-based probability control for improved parsing accuracy.