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Actionable sentences are terms that, in the most basic sense, imply thenecessity of taking a specific action. In Linguistic terms, they are steps toachieve an operation, often through the usage of action verbs. For example, thesentence, `Get your homework finished by tomorrow` qualifies as actionablesince it demands a specific action (In this case, finishing homework) to betaken. In contrast, a simple sentence such as, `I like to play the guitar` doesnot qualify as an actionable phrase since it simply states a personal choice ofthe person instead of demanding a task to be finished.  In this paper, the aim is to explore if Actionables can be extracted from rawtext using Linguistic filters designed from scratch. These filters arespecially catered to identifying actionable text using Transfer Learning as thelead role. Actionable Detection can be used in detecting emergency tasks duringa crisis, Instruction accuracy for First aid and can also be used to makeproductivity tools like automatic ToDo list generators from conferences. Toaccomplish this, we use the Enron Email Dataset and apply our Linguisticfilters on the cleaned textual data. We then use Transfer Learning with theUniversal Sentence Encoder to train a model to classify whether a given stringof raw text is actionable or not.

Actionable Phrase Detection using NLP