Recent advancements in dependency parsing include combining graph-based and transition-based methods for better performance, using neural network models to capture complex ...
Recent advancements in dependency parsing include combining graph-based and transition-based methods for better performance, using neural network models to capture complex token relationships, and exploring unsupervised parsing to help with low-resource languages. These improvements make parsers more accurate and efficient, boosting the effectiveness of NLP applications. Have you tried any of these new techniques in your research?