Disambiguating Terminal Punctuation Identification of Elucidating Period Using Machine Learning Techniques in English Linguistic Processing

In natural text, a period is a punctuation, also known as a ”full stop" in British English. It appears like- "." or we can say dot or tiny circle. It is more ambiguous, meaning it is similarly most unclear or unresolved. Sentence boundary detection is the principal problem in period disambiguation. Periods are used to end sentences and for multiple other uses, including-URLs, abbreviations, and designations. Therefore, resolving period disambiguity is a crucial challenge in Natural Language Processing. In this paper, we proposed to classify the problem of disambiguity in natural texts through various supervised machine learning models. Several features were recognized between sentence-ending and nonsentence-ending periods. Features were extracted from the dataset from the different newspapers from online news web portals. The classes were labeled in that dataset. Different supervised learning algorithms were implemented in the dataset to predict the classes. This research aims to allow machines to perceive the context of periods, thereby having beneficial effects on Natural Language Processing, tests, and sentence analysis.