Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Gauch, Susan

Committee Member/Reader

Patitz, Matthew

Committee Member/Second Reader

Gauch, John

Abstract

There have been a multitude of word embedding techniques developed that allow a computer to process natural language and compare the relationships between different words programmatically. In this paper, similarity analysis, or the testing of words for synonymic relations, is used to compare several of these techniques to see which performs the best. The techniques being compared all utilize the method of creating word vectors, reducing words down into a single vector of numerical values that denote how the word relates to other words that appear around it. In order to get a holistic comparison, multiple analyses were made, with the WOVe technique performing the best overall at producing both the most synonyms and the most accurate synonyms.

Keywords

Word embedding, natural language processing, GloVe, Word2Vec, similarity analysis, synonyms

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