A Literature Review: Stemming Algorithms for Indian Languages.
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paper_title: Stemming algorithms: a case study for detailed evaluation

paper_content:

The majority of information retrieval experiments are evaluated by measures such as average precision and average recall. Fundamental decisions about the superiority of one retrieval technique over another are made solely on the basis of these measures. We claim that average performance figures need to be validated with a careful statistical analysis and that there is a great deal of additional information that can be uncovered by looking closely at the results of individual queries. This article is a case study of stemming algorithms which describes a number of novel approaches to evaluation and demonstrates their value. © 1996 John Wiley & Sons, Inc.
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paper_title: Discovering suffixes: A Case Study for Marathi Language

paper_content:

Suffix stripping is a pre-processing step required in a number of natural language processing applications. Stemmer is a tool used to perform this step. This paper presents and evaluates a rule-based and an unsupervised Marathi stemmer. The rule-based stemmer uses a set of manually extracted suffix stripping rules whereas the unsupervised approach learns suffixes automatically from a set of words extracted from raw Marathi text. The performance of both the stemmers has been compared on a test dataset consisting of 1500 manually stemmed word.
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Title: A Literature Review: Stemming Algorithms for Indian Languages

Section 1: INTRODUCTION
Description 1: Write an introduction about the relevance of a literature review in the study of stemming algorithms for Indian languages and the key concepts addressed such as Data Mining, Text Mining, Stemming, and Clustering.

Section 2: STEMMING: A REVIEW
Description 2: Summarize various stemming algorithms and significant studies from prior years relevant to stemming and their impact on Information Retrieval Systems (IRS).

Section 3: STEMMERS FOR INDIAN LANGUAGES: A REVIEW
Description 3: Review the literature specific to stemming algorithms developed for various Indian languages, detailing methods and evaluations conducted on languages like Hindi, Marathi, Telugu, Gujarati, and Tamil.

Section 4: CONCLUSION
Description 4: Discuss the role and impact of stemming in information retrieval systems, summarize the findings from the literature review, and suggest future directions for research in stemming algorithms for Indian languages.