IMPROVED PERFORMANCE OF STEMMING USING ENHANCED PORTER STEMMER ALGORITHM FOR INFORMATION RETRIEVAL

In the era of digitalization, information retrieval (IR) are retrieves and ranks documents from large  collections according to users search queries, has been usually applied in the several domains. Building records using electronic and searching literature for topics of interest are some IR use cases. For the moment, Natural Language Processing (NLP), such as tokenization, stop word removal and stemming or Part-Of-Speech (POS) tagging, has been developed for processing documents or literature. This study offer that NLP can be incorporated into IR to strengthen the conventional IR models. In this paper proposed Enhanced Porter Stemmer algorithm for improving the efficiency of pre-processing in text mining. The Enhanced Porter Stemmer algorithm is extension version of new porter stemmer. The Enhanced Porter Stemmer algorithm performance is compared with several algorithms such as porter, new porter and etc. The performance of the Enhanced Porter Stemmer is better than others.