Date of Submission

Spring 2024

Academic Program

Computer Science

Project Advisor 1

Kerri-Ann Norton

Project Advisor 2

Sven Anderson

Abstract/Artist's Statement

Lexical Simplification is the process of replacing complex words with simpler alternatives in a given text. This project aims to use different approaches in the field of Natural Language Processing to create a series of lexical Simplification models. The framework of lexical simplifiers will also be explored and researched, to give more insight of the mechanisms and approaches used to achieve successful text simplification. I will develop a pipeline of steps, based on my research, with the aim to create a framework for a functional lexical simplification model. I will develop a series of distinct lexical simplification models based on my pipeline, with the expectation of creating an ideal lexical simplification model. The models I develop will be tested and compared to one another to find strengths and weaknesses for potential improvements. The performance of my models were measured utilizing the Chi-Squared metric for three hypothesis tests that aim to assess the impact of my models. After analysis the Text Frequency Thesaurus Simplification model was superior in correctly identifying complex terms as well as potentially having the best approach for generation. Although the tests were very insightful, more testing and analysis can potentially yield better results for comparison. I will also discuss potential improvements intended to solve some of the weaknesses of my models for future iterations.

Open Access Agreement

Open Access

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional Files
TheB4.zip (16 kB)
Dictonarydta.zip (7421 kB)
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