Evaluation of some Information Retrieval models for Gujarati Ad hoc Monolingual Tasks

TL;DR

This study evaluates various Information Retrieval models for Gujarati monolingual tasks, finding classical models like TF_IDF outperform recent probabilistic models, providing insights into effective IR approaches for Gujarati language processing.

Contribution

It presents a baseline for Gujarati IR and compares multiple models, highlighting the superior performance of classical IR models over recent probabilistic ones.

Findings

TF_IDF outperforms recent probabilistic models for Gujarati IR

Established a baseline with MAP values for Gujarati IR models

Identified the most effective IR models for Gujarati language

Abstract

This paper describes the work towards Gujarati Ad hoc Monolingual Retrieval task for widely used Information Retrieval (IR) models. We present an indexing baseline for the Gujarati Language represented by Mean Average Precision (MAP) values. Our objective is to obtain a relative picture of a better IR model for Gujarati Language. Results show that Classical IR models like Term Frequency Inverse Document Frequency (TF_IDF) performs better when compared to few recent probabilistic IR models. The experiments helped to identify the outperforming IR models for Gujarati Language.