Natural Language Processing (NLP) is a dynamic field within Artificial Intelligence focused on developing computational models to understand, interpret, and generate human language. As NLP technologies become increasingly embedded in our daily lives, understanding its fundamentals is crucial for both leveraging its potential and enhancing our interaction with language-based systems.

This course is designed to build a robust foundation in the core principles of modern NLP. We begin with text processing techniques that show how to manipulate textual data for model development. We then progress to language models, enabling computational models to comprehend and generate human language, and vector space models that convert textual data into machine-readable formats. Advanced topics include distributional semantics for creating context-aware word embeddings, and contextual encoding for analyzing word relationships within their surrounding text.

The latter part of the course focuses on practical application through team projects. Students will have the opportunity to work with cutting-edge NLP technologies, such as large language models, to develop real-world applications. This hands-on approach encourages creativity and innovation, with students proposing their own ideas and presenting demonstrations to their peers.

Learning assessment combines concept quizzes to reinforce theoretical understanding with hands-on programming assignments that develop practical implementation skills. By the conclusion of the course, students will have gained the knowledge and skills to navigate and contribute to the rapidly evolving field of NLP.

Prerequisites

• Introduction to Python Programming

• Introduction to Machine Learning

Sections

This guide will help you set up your development environment by installing required tools: Python programming language, GitHub for version control, and PyCharm IDE.

Python

• Install Python version 3.14.x or higher. Earlier versions may not be compatible with this course.

• Take some time to familiarize yourself with Python's .

GitHub

1. Create a account (if you do not already have one). As a student, you can get features for free through the .

2. Login to GitHub.

3. Create a new repository named "nlp-essentials" and set its visibility to Private.

PyCharm

1. Install on your local machine:

   1. As a student, you can get for free by applying for a .

   2. The following instructions are based on PyCharm 2024.3.x Professional Edition.

1. Create a new PyCharm project from GitHub:

   1. On the PyCharm welcome screen, click [Clone Repository].

   2. In the new window, select [GitHub] from the left menu, choose your nlp-essentials

References

• : a version control system for tracking changes in files.

• : a tool to create isolated Python environment.

General

• GitHub: https://github.com/emory-courses/nlp-essentials/

• Book:

• Time: MW 2 - 3:45 PM

• Location: MSC W201

Instructors

• • Associate Professor of Computer Science, Data and Decision Sciences, Linguistics

  • Office Hours and Location: MW 4:00 - 5:00 PM, Emerson Hall 500

Grading

• Homework: 65%

• Team Project: 35%

• Your work is governed by the . Honor code violations (e.g., copies from any source, including colleagues and internet sites) will be referred to the Emory Honor Council.

Homework

• Each topic will include homework that combines concept quizzes and programming assignments to assess your understanding of the subject matter.

• Assignments must be submitted individually. While discussions are allowed, your work must be original.

• Late submissions within a week will be accepted with a grading penalty of 15%. They will not be accepted after the solutions are discussed in class.

Concept Quizzes

• Each section incorporates questions to explore the content more comprehensively, with their corresponding answers slated for discussion in the class.

• While certain questions may have multiple valid answers, the grading will be based on the responses discussed in class, and alternative answers will be disregarded. This approach allows us to distinguish between answers discussed in class and those generated by AI tools like ChatGPT.

Programming Assignments

• You are encouraged to use any code examples and invoke APIs provided in this book.

• Feel free to create additional functions and variables in the assigned Python file. For each homework, ensure that all your implementations are included in the respective Python file located under the corresponding directory.

• Usage of packages not covered in the assigned chapter is prohibited. Ensure that your code does not rely on the installation of additional packages, as we will not be able to execute your program for evaluation if external dependencies are needed.

Team Project

• You are expected to:

  • Form a team of 3-4 members.

  • Present a to share your proposed idea, and write a .

Project Grading

• Team members receive the same grade for the pitch presentation, live demonstration, and demonstration video.

• Peer evaluations from other teams factor into your team grade.

• Your feedbacks to other teams are graded individually.

For the project and final reports, you are required to indicate the contribution percentage of each team member, which impacts the individual grades for the assignment.

Contribution

If your team of two members received 4 out of 5 points, for example, and you indicate that your contribution was 60% while your teammate's was 40%, the points are distributed as follows:

• You receive: 4 (team points) ⨉ 60 (your contributions) / 60 (max contributions) = 4 points.

• Your teammate receive: 4 (team points) ⨉ 40 (your teammate's contributions) / 60 (max contributions) = 2.67 points.

This approach ensures that the grading reflects the effort and input of each team member, promoting fairness and accountability.

Consensus

• Each team is required to submit a single, agreed-upon chart detailing the contribution percentages of all members for each team assignment. This means that you and your teammates must reach a consensus on the contribution rates before submitting your work.

• Open communication and transparency are essential in this process. Disagreements should be resolved within the team, ensuring that the final submission reflects the true division of labor and contributions.

By adhering to these guidelines, you not only produce a strong research paper but also develop key skills in teamwork and fair assessment of contributions.

Task 1: Getting Started

In this assignment, you will:

1. Set up your development environment,

2. Install your first Python package using pip within a virtual environment,

3. Run a test program to verify the installation and environment configuration, and

4. Commit your changes and push them to your GitHub repository.

This will ensure your development workspace is properly configured for this course.

Package Installation

Once you set up the :

1. Open Terminal in PyCharm:

   1. Click the Terminal icon () at the bottom left, or

   2. Select the [View] > [Tool Windows] > [Terminal] menu).

Test Program

1. Create the project structure:

   1. Create a new Python package called in your nlp-essentials directory.

   2. Inside src, create a package.

Commit & Push

1. Create a file:

   1. Create the file in your nlp-essentials root directory

   2. Add the following lines to exclude unnecessary files:

Submission

Submit the URL of your GitHub repository to Canvas.

Task 2: Project Ideas

Share your team project concept by filling out the form in Canvas (about 100-150 words). Your description will be posted on the page to help classmates discover shared interests and form teams.

Rubric

• GitHub Setup (0.2 points):

  • Private repository created.

  • All instructors added as collaborators.

• Attendance is mandatory for all Project Pitches and Live Demonstrations sessions.