Rule-Based Dependency Parsing Using Artificial Neural Networks
Domestic Conferences & Theses
Authors
Joongmin Shin*, Sanghyun Cho, Bongwoo Nam, Hyuk-Chul Kwon
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.