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The identification of proper nouns from text aims to classify named entities according to their respective groupings, an aspect included in Named Entity Recognition (NER). Proper noun disambiguation can adversely affect morphological analysis, a vital trait to improve the corpus availability via classification and new word assimilation. The occurrences of proper nouns can be annotated from the text resources using separate entity mapping from their fragments. The research was carried out to examine the impact of regex on text pattern identification sequence that queried and acquired proper nouns from a collection of unannotated Malay language news articles that envisions several techniques to improve text entities precision and accuracy such as pre-processing and data clustering. The results showed that the F-scores of the output tested on the unannotated news dataset were between 30% and 60%

Feature extraction using regular expression in detecting proper noun for Malay news articles based on KNN algorithm