Abstract
This paper explores the integration of advanced NLP techniques—text embeddings, neural networks, and transformer-based LLMs—in the WooScholar App, a tool for natural language search of Independent Study theses. We discuss key methods for word vectorization (e.g., one-hot encoding, Word2Vec, GloVe) and similarity measures (such as cosine distance), and outline neural network architectures and training processes. The study also highlights transformer models that use self-attention for coherent text generation. These concepts are applied in the WooScholar App, built with FastAPI, PostgreSQL, and Next.js, and enhanced by Google’s Gemini API, demonstrating the effective combination of theoretical NLP advancements with practical application development.
Advisor
Visa, Sofia
Department
Computer Science
Recommended Citation
Yang, Soobin, "WooScholar App: Harnessing Text Embeddings and LLM for Finding Independent Study Theses" (2025). Senior Independent Study Theses. Paper 11595.
https://openworks.wooster.edu/independentstudy/11595
Disciplines
Other Computer Engineering
Keywords
Text embedding, Neural Networks, Transformer Architecture, Large Language Models, Retrieval-Augmented Generation
Publication Date
2025
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
© Copyright 2025 Soobin Yang