<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sahu-pal-2025-study">
    <titleInfo>
        <title>A study on the language independent stemmer in the Indian language IR</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Siba</namePart>
        <namePart type="given">Sankar</namePart>
        <namePart type="family">Sahu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sukomal</namePart>
        <namePart type="family">Pal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sudhansu</namePart>
            <namePart type="given">Bala</namePart>
            <namePart type="family">Das</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pruthwik</namePart>
            <namePart type="family">Mishra</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alok</namePart>
            <namePart type="family">Singh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shamsuddeen</namePart>
            <namePart type="given">Hassan</namePart>
            <namePart type="family">Muhammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Asif</namePart>
            <namePart type="family">Ekbal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Uday</namePart>
            <namePart type="given">Kumar</namePart>
            <namePart type="family">Das</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, BULGARIA</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We explore and evaluate the effect of different language-independent stemmers in the information retrieval (IR) tasks with Indian languages such as Hindi, Gujarati, and English. The issue was examined from two points of view. Does a language-independent stemmer improve retrieval effectiveness in Indian languages IR? Which language-independent stemmer is the most suitable for different Indian languages? It is observed that stemming enhances retrieval efficiency in different Indian languages compared to the no stemming approaches. Among the different stemmers experimented with, the co-occurrence-based stemmer (SNS) performs the best and improves a mean average precision (MAP) score by 2.98% in Hindi, and 20.78% in Gujarati languages, respectively, whereas the graph-based stemmer (GRAS) performs the best and improves a MAP score by 5.83% in English.</abstract>
    <identifier type="citekey">sahu-pal-2025-study</identifier>
    <location>
        <url>https://aclanthology.org/2025.globalnlp-1.20/</url>
    </location>
    <part>
        <date>2025-09</date>
        <extent unit="page">
            <start>181</start>
            <end>189</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A study on the language independent stemmer in the Indian language IR
%A Sahu, Siba Sankar
%A Pal, Sukomal
%Y Das, Sudhansu Bala
%Y Mishra, Pruthwik
%Y Singh, Alok
%Y Muhammad, Shamsuddeen Hassan
%Y Ekbal, Asif
%Y Das, Uday Kumar
%S Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, BULGARIA
%C Varna, Bulgaria
%F sahu-pal-2025-study
%X We explore and evaluate the effect of different language-independent stemmers in the information retrieval (IR) tasks with Indian languages such as Hindi, Gujarati, and English. The issue was examined from two points of view. Does a language-independent stemmer improve retrieval effectiveness in Indian languages IR? Which language-independent stemmer is the most suitable for different Indian languages? It is observed that stemming enhances retrieval efficiency in different Indian languages compared to the no stemming approaches. Among the different stemmers experimented with, the co-occurrence-based stemmer (SNS) performs the best and improves a mean average precision (MAP) score by 2.98% in Hindi, and 20.78% in Gujarati languages, respectively, whereas the graph-based stemmer (GRAS) performs the best and improves a MAP score by 5.83% in English.
%U https://aclanthology.org/2025.globalnlp-1.20/
%P 181-189

Markdown (Informal)

[A study on the language independent stemmer in the Indian language IR](https://aclanthology.org/2025.globalnlp-1.20/) (Sahu & Pal, GlobalNLP 2025)

• A study on the language independent stemmer in the Indian language IR (Sahu & Pal, GlobalNLP 2025)

ACL

• Siba Sankar Sahu and Sukomal Pal. 2025. A study on the language independent stemmer in the Indian language IR. In Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models, pages 181–189, Varna, Bulgaria. INCOMA Ltd., Shoumen, BULGARIA.