CSCI544 - Applied Natural Language Processing - Fall 23 - USC

Welcome to the comprehensive repository for CSCI 544 - Applied Natural Language Processing at USC during the Fall 2023 semester, taught by Professor Mohammad Rostami. This centralized hub contains all coursework materials, including assignments and project solutions, organized into folders representing distinct modules covered in the course.

Within this repository, you'll find assignments, quizzes, and projects-related materials and solutions that delve into machine learning and deep learning algorithms applied to real-world datasets. It's important to use this repository responsibly, refraining from engaging in plagiarism-related activities.

Tip

Before exploring the materials, take a moment to review the license and disclaimer for responsible utilization. The repository covers various topics, providing valuable insights and hands-on experience in Natural Language Processing.

Course Details:

• Course Name: CSCI 544 - Applied Natural Language Processing
• Instructor: Professor Mohammed Rostami
• Semester: Fall 2023
• Focus Areas:
  • Sentiment Analysis on the Amazon Reviews Dataset using Classical ML models (Homework 1)
  • Part-of-Speech (POS) Tagging on the Wall Street Journal (WSJ) Dataset using Hidden Markov Models + Greedy and Viterbi Decoding (Homework 2)
  • Sentiment Analysis of Amazon Reviews Dataset using Word2Vec Embeddings and Neural Networks (Homework 3)
  • Named Entity Recognition (NER) on CoNLL-2003 corpus dataset (Homework 4)
  • Paper Presentation on "A Static Evaluation of Code Completion by Large Language Models"
  • Project Proposal, Presentation, and Final Project on "Leveraging static analysis for evaluating code-generation models"

Feel free to explore the assignments, projects, and solutions provided as learning aids. Whether you're a beginner or an experienced practitioner, this repository aims to be your companion in mastering the intersection of machine learning and data science within Natural Language Processing. Happy learning!

Caution

Please note that this repository serves as a reference guide and should be utilized as a tool for learning and comprehension. It's paramount to refrain from engaging in any activities associated with plagiarism. Embrace the wealth of knowledge herein to enhance your understanding and augment your skill set in the field of machine learning.

Table of contents

Assignment	Topic Covered	Grade
Homework 1	Sentiment Analysis on Amazon Reviews Dataset using Classical ML models	100/100
Homework 2	Part-of-Speech (POS) Tagging on Wall Street Journal (WSJ) Dataset using Hidden Markov Models + Greedy and Viterbi Decoding	100/100
Homework 3	Sentiment Analysis of Amazon Reviews Dataset using Word2Vec Embeddings and Neural Networks	100/100
Homework 4	Named Entity Recognition (NER) on CoNLL-2003 corpus dataset	100/100
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Quizzes	Solutions for quizzes + midterm + final exam
Paper Presentation	A Static Evaluation of Code Completion by Large Language Models
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Final Project	Leveraging static analysis for evaluating code-generation models (Project Proposal + Presentation + Source Code)

Note

Overall Grade: A

Authors

1. Kayvan Shah | MS in Applied Data Science | USC

LICENSE

This repository is licensed under the BSD 5-Clause License. See the LICENSE file for details.

Disclaimer

_{The content and code provided in this repository are for educational and demonstrative purposes only. The project may contain experimental features, and the code might not be optimized for production environments. The authors and contributors are not liable for any misuse, damages, or risks associated with the use of this code. Users are advised to review, test, and modify the code to suit their specific use cases and requirements. By using any part of this project, you agree to these terms.}