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One of the prominent problems with processing and operating on text data isthe non uniformity of it. Due to the change in the dialects and languages, thecaliber of translation is low. This creates a unique problem while using NLP intext data; which is the spell variation arising from the inconsistenttranslations and transliterations. This problem can also be further aggravatedby the human error arising from the various ways to write a Proper Noun from anIndian language into its English equivalent. Translating proper nounsoriginating from Indian languages can be complicated as some proper nouns arealso used as common nouns which might be taken literally. Applications of NLPthat require addresses, names and other proper nouns face this problemfrequently. We propose a method to cluster these spell variations for propernouns using ML techniques and mathematical similarity equations. We aimed touse Affinity Propagation to determine relative similarity between the tokens.The results are augmented by filtering the token-variation pair by a similaritythreshold. We were able to reduce the spell variations by a considerableamount. This application can significantly reduce the amount of humanannotation efforts needed for data cleansing and formatting.

Clustering of Spell Variations for Proper Nouns Transliterated from the other languages