Abstract
Now a day’s text documents are advancing over internet, e-mails and web pages. As the use of internet is exponentially growing, the need of massive data storage is increasing from time to time. Normally many of the documents contain morphological variables, so stemming which is a preprocessing technique gives a mapping of different morphological variants of words into their base word called the stem. Stemming process is used in information retrieval applications accordingly as a way to improve retrieval performance based on the assumption that terms with the same stem usually have similar meaning. To do stemming operation on bulky documents, we require normally more computation time and power, to cope up with the need to search for a particular word in the data. In this paper, various stemming algorithms are analyzed with the benefits and limitation of the recent stemming methods or approaches. Keywords : - Natural Language Processing Applications, Information Retrieval, Information Retrieval Applications (IRAs), Stemming Approaches DOI: 10.7176/JIEA/10-3-01 Publication date: April 30 th 2020
Highlights
In all Information Retrieval applications, the main thing is to improve recalls and precisions
Stemming is a preprocessing footstep in text mining applications as well as a very common requirement of natural language processing functions
The capacity of the search database has increased in the last few years, so in order to meet the challenge of real time search natural language application algorithms speed up required
Summary
In all Information Retrieval applications, the main thing is to improve recalls and precisions . 2. Working Principles of Stemmer It has been seen that most of the times the morphological variants of words have similar semantic interpretations and can be considered as equivalent for the purpose of information retrieval applications. The idea is to decrease the total number of dissimilar terms in a file or a query which in turn will decrease the processing time of the final output for any kind of information retrieval applications (Demilie 2019).
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