Sarbajit Roy

ORCID Identifier(s)

0000-0001-8879-1641

Graduation Semester and Year

2019

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Chengkai Li

Abstract

News headlines around the world are alarming. They aim to trigger emotional responses from the consumer. In an era where news and social media posts spread so quickly, it is often difficult to distinguish between what is real and what is false. Human fact-checking fails to cope with the growth of such unprecedented information, thus increasing the demand and various steps of automatic fact-checking to judge the veracity of claims. An automatic fact-checking system is a statistical model that can help to detect misinformation. The current state-of-the-art frame semantic parsers suffer from lack of a large annotated data-set and there are limited annotation tools available. So, we introduce how Frame Annotator, a web-based frame semantic annotation tool, uses natural language processing and the concept of frame semantics to help users to generate annotated data-sets.

Keywords

FrameAnnotator, Semantic role labeling, Fact-checking, IDIR, Frames

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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