AD – Elog Data Search Using Natural Language Processing Techniques

Abstract

The goal of this project is to develop and evaluate a Machine Learning model using natural language processing techniques to categorize AD E-Log entries efficiently. AD E-Logs, which play a vital role in Fermilab's projects as a system to record and manage various data, often suffer from slow search processing and imprecise results. To address this issue, we preprocessed the data and employed the Doc2Vec model for training. The resulting model enables us to identify and retrieve the most relevant entries, thus enhancing user experience in accessing desired information.