Word Sense Disambiguation in Persian: Can AI Finally Get It Right?

TL;DR

This paper introduces a new Persian homograph disambiguation dataset, evaluates various embeddings and models, and provides insights to improve AI's ability to distinguish words with identical spellings but different meanings.

Contribution

It presents a novel Persian dataset, compares embedding effectiveness, and benchmarks multiple models for homograph disambiguation, advancing NLP in Persian language processing.

Findings

New Persian homograph dataset created

Embeddings vary in effectiveness across tasks

Model benchmarking offers guidance for future research

Abstract

Homograph disambiguation, the task of distinguishing words with identical spellings but different meanings, poses a substantial challenge in natural language processing. In this study, we introduce a novel dataset tailored for Persian homograph disambiguation. Our work encompasses a thorough exploration of various embeddings, evaluated through the cosine similarity method and their efficacy in downstream tasks like classification. Our investigation entails training a diverse array of lightweight machine learning and deep learning models for phonograph disambiguation. We scrutinize the models' performance in terms of Accuracy, Recall, and F1 Score, thereby gaining insights into their respective strengths and limitations. The outcomes of our research underscore three key contributions. First, we present a newly curated Persian dataset, providing a solid foundation for future research in homograph disambiguation. Second, our comparative analysis of embeddings highlights their utility in different contexts, enriching the understanding of their capabilities. Third, by training and evaluating a spectrum of models, we extend valuable guidance for practitioners in selecting suitable strategies for homograph disambiguation tasks. In summary, our study unveils a new dataset, scrutinizes embeddings through diverse perspectives, and benchmarks various models for homograph disambiguation. These findings empower researchers and practitioners to navigate the intricate landscape of homograph-related challenges effectively.