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