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This paper presents an integrated language model to improve document relevancy for text-queries. To be precise, an integrated stemming-lemmatization (S-L) model was developed and its retrieval performance was compared at three document levels, that is, at top 5, 10 and 15. A prototype search engine was developed and fifteen queries were executed. The mean average precisions revealed the S-L model to outperform the baseline (i.e. no language processing), stemming and also the lemmatization models at all three levels of the documents. These results were also supported by the histogram precisions which illustrated the integrated model to improve the document relevancy. However, it is to note that the precision differences between the various models were insignificant. Overall the study found that when language processing techniques, that is, stemming and lemmatization are combined, more relevant documents are retrieved.