Abstract
This article presents new methods for the study of language evolutions which helps researchers and experts. Initially, a method is used to determine if the words are cognate or not. A linguistic information algorithm is proposed to derive cognates from online dictionaries. Later, a dataset is created of similar terms and machine learning techniques are used to focus on spelling in order to classify the cognates. The aligned subsequences are used to identify standards and guidelines for language change in newly created languages mainly to distinguish between non-cognates and cognates which are used for classification algorithms. Next, discriminating cognates and debts give an insight into a language's history and allow a clearer understanding of the linguistic relationship. The task of reconstruction of protowords is to recreate words from its modern daughter languages in an ancient language. The method is based on the regularity of words and use knowledge from many modern languages to build an ensemble method for protoword reconstruction. This method is applied to multiple datasets to improve from the previous dataset accuracies.
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