Leveraging LLMs for English Dependency Parsing

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This coursework explores dependency parsing using large language models (LLMs), examining existing research and limitations of current methods. It analyses linguistic error patterns, compares closed vs open-weight LLMs, and evaluates models using specific datasets and metrics. Examples include GPT-4o's few-shot prompting performance and self-correction mechanisms. Concluding insights highlight findings, suggest hybrid parsing approaches, and discuss implications of the study.