Recent advances in natural language processing with large neural models have opened new possibilities for syntactic analysis based on machine learning. This work explores a novel approach to phrase-structure analysis by fine-tuning large language models (LLMs) to translate an input sentence into its corresponding syntactic structure. The main objective is to extend the capabilities of MiSintaxis, a tool designed for teaching Spanish syntax. Several models from the Hugging Face repository were fine-tuned using training data generated from the AnCora-ES corpus, and their performance was evaluated using the F1 score. The results demonstrate high accuracy in phrase-structure analysis and highlight the potential of this methodology.
@article{arxiv.2510.16604,
title = {Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach},
author = {Francisco Jose Cortes Delgado and Eduardo Martinez Gracia and Rafael Valencia Garcia},
journal= {arXiv preprint arXiv:2510.16604},
year = {2025}
}
Comments
6 pages, 3 figures. Submitted to SEPLN 2023 Conference