  Flip through any book or listen to any song lyrics, and you will come across
pronouns that, in certain cases, can hinder meaning comprehension, especially
for machines. As the role of having cognitive machines becomes pervasive in our
lives, numerous systems have been developed to resolve pronouns under various
challenges. Commensurate with this, it is believed that having systems able to
disambiguate pronouns in sentences will help towards the endowment of machines
with commonsense and reasoning abilities like those found in humans. However,
one problem these systems face with modern English is the lack of gender
pronouns, where people try to alternate by using masculine, feminine, or plural
to avoid the whole issue. Since humanity aims to the building of systems in the
full-bodied sense we usually reserve for people, what happens when pronouns in
written text, like plural or epicene ones, refer to unspecified entities whose
gender is not necessarily known? Wouldn't that put extra barriers to existing
coreference resolution systems? Towards answering those questions, through the
implementation of a neural-symbolic system that utilizes the best of both
worlds, we are employing PronounFlow, a system that reads any English sentence
with pronouns and entities, identifies which of them are not tied to each
other, and makes suggestions on which to use to avoid biases. Undertaken
experiments show that PronounFlow not only alternates pronouns in sentences
based on the collective human knowledge around us but also considerably helps
coreference resolution systems with the pronoun disambiguation process.
