A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis

TL;DR

This paper introduces a benchmark corpus for Sanskrit suffixes and inflectional words, along with a neural network approach for analyzing derivative nouns, aiming to standardize and improve morphological analysis tools.

Contribution

It provides the first benchmark corpus for Sanskrit derivative nouns and proposes a neural method for their analysis, enhancing existing linguistic tools.

Findings

The benchmark corpus enables standardized evaluation of Sanskrit morphological tools.

The neural approach outperforms existing tools in derivative noun analysis.

The corpus and method are publicly available for research use.

Abstract

This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and inflectional words (padas) formed due to suffixes along with neural network based approaches to process the formation and splitting of inflectional words. Inflectional words spans the primary and secondary derivative nouns as the scope of current work. Pratyayas are an important dimension of morphological analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics tools for processing and analyzing Sanskrit texts. Unfortunately there has not been any work to standardize & validate these tools specifically for derivative nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus called Pratyaya-Kosh to evaluate the performance of tools. We also present our own neural approach for derivative nouns analysis while evaluating the same on most prominent Sanskrit Morphological Analysis tools. This benchmark will be freely dedicated and available to researchers worldwide and we hope it will motivate all to improve morphological analysis in Sanskrit Language.