Publications / Domestic Conferences & Theses
A Neural-Symbolic Model for Overcoming Deep Learning Limitations in Korean Dependency Parsing
Abstract
Proposed and analyzed a novel neural-symbolic model that controls final probability values based on additional linguistic knowledge to overcome the limitations of overfitting and data scarcity issues arising from deep learning's dependence on datasets.
Key Contribution
Neural-symbolic parser integrating linguistic constraints to overcome deep learning limitations in dependency parsing.