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
Citation
Gerth, T. (2021). A Comparison of Word Embedding Techniques for Similarity Analysis. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/85
Included in
Numerical Analysis and Scientific Computing Commons, Other Computer Sciences Commons, Theory and Algorithms Commons