VisNLP 2.0: A Visual-Based Educational Support Platform for Teaching and Learning Neural Network-Based NLP Analytics
Jack Gomes, Daniel Johnson, Garrett McMerriman, John Prominski, Henry Yoder
Worcester Polytechnic Institute, 2023.
Natural Language Processing (NLP) has become increasingly relevant in our data-driven world and is being implemented widely today due to the abundance of natural language data. However, mastering NLP processes is not a simple task, particularly for those without a comprehensive background in the field. Further, the use of neural networks in many commonly used NLP approaches makes it difficult to interpret the input-output relationships of these models, creating the "black-box" problem in data science. To address these challenges, we develop and implement a web-based interactive visual NLP learning platform that enables learners to study some fundamental neural network-based NLP techniques, topics, and applications. Specifically, the technical contribution of this work is threefold: (1) To present popular neural network-based NLP analytics methods in a step-by-step linear format that is easy to comprehend. (2) To eliminate the 'black box' problem found in neural network-based NLP learning resources by providing continuous real-data examples. (3) To enable users to interpret model outputs through interactive visual demos that apply neural network-based NLP method outputs.
This project is part of a Major Qualifying Project submitted to the faculty of Worcester Polytechnic Institute in partial fulfillment of the requirements for the Degrees of Bachelor of Science in Computer Science and Data Science.
Created By: Jack Gomes (DS), Daniel Johnson (DS), Garrett McMerriman (DS), John Prominski (CS), Henry Yoder (CS)
Advised By: Professor Chun-Kit Ngan, WPI (DS)
