Electronic Theses and Dissertations

Date of Award


Document Type


Degree Name

M.S. in Engineering Science

First Advisor

Naeemul Hassan

Second Advisor

Yixin Chen

Third Advisor

Kristen A. Swain


University of Mississippi

Relational Format



This study introduces a new Natural Language Generation (NLG) task – Unit Claim Identification. The task aims to extract every piece of verifiable information from a headline. The Unit Claim identification has applications in other domains; such as fact-checking where the identification of each verifiable information from a check-worthy statement can lead to an effective fact-check. Moreover, the extracting of the unit claims from headlines can identify a misleading news article, by mapping evidence from contents. For addressing the unit claim identification problem, we outlined a set of guidelines for data annotation, arranged in-house training for the annotators and obtained a small dataset. We explored two potential approaches - 1) Rule-based approach and 2) Deep learning-based approach and compared their performances. Although the performance of the deep learning-based approach was not very effective due to small number of training instances, the rule-based approach shoa promising result in terms of precision (65.85%).



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