PronounFlow: A Hybrid Approach for Calibrating Pronouns in Sentences
Nicos Isaak

TL;DR
PronounFlow is a neural-symbolic system designed to disambiguate and calibrate pronouns in sentences, improving machine understanding and coreference resolution by addressing gender biases and unspecified entities.
Contribution
It introduces a hybrid neural-symbolic approach for pronoun calibration that enhances disambiguation and supports coreference resolution in English texts.
Findings
PronounFlow effectively alternates pronouns based on human knowledge.
It significantly improves coreference resolution accuracy.
The system handles gender biases and unspecified entities well.
Abstract
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…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Text Readability and Simplification
