Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems

Abstract:With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.34138 [cs.CL]

(or arXiv:2609.34138v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.34138 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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