MA-INF 4237: NATURAL LANGUAGE PROCESSING LAB

Summer Semester 2024

Content:

What is the NLP Lab course about?

The Natural Language Processing (NLP) Lab course provides students with a detailed look at the recent advancements in NLP, covering various aspects such as large language models (LLMs), conversational systems, and computational social science. 

Through tutorials and a final project, you will gain practical skills in NLP techniques and have this chance to apply this knowledge to a various interesting project. Students will collaborate in small teams (a group of two students) and implement NLP applications over the course of the term. Each team is advised by one researcher of the CAISA Lab.
 

Mandatory participation requirements:

Have completed at least one of the following lectures:

  • MA-INF 4115: Introduction to Natural Language Processing
  • MA-INF 4238: Dialogue Systems
     

Recommendedparticipation requirements:

  • Basic programming knowledge in Python and Machine Learning
  • Basics of Machine Learning
  • Basic knowledge of Python Libraries for ML (NumPy, Scikit-Learn, Pandas)
  • Basics of Probability, Linear Algebra and Statistics

Logistics:

  • Tutorials (3 weeks): are on Wednesdays 4 PM -  6 PM in 2.113 (Friedrich-Hirzebruch-Allee 6).
     
  • Project Meetings: Depending on the project topic you choose, each team (a group of 2 students) will set up regular meetings with the advisor to discuss the progress of the project and get feedback.
     
  • Lab Materials: will be uploaded every week on eCampus.
     
  • Contact: Students should ask all the course-related questions in our forum discussion on eCampus. For external inquiries, emergencies, or personal matters, you can email us at nlplab.uni.bonn@gmail.com.
     
  • Office Hours: Please reach out to us first via mail to arrange any in-person meeting.
    • Prof. Dr. Lucie Flek: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.123
    • Vahid Sadiri Javadi: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.120

NEWS / UPDATES:

  • 25.02.2024: The first kick-off meeting takes place on Wednesday, 17.04.2024 at 04:15 PM in Room 2.113.

Instructors:

Prof. Dr. Lucie Flek

flek(at)bit.uni-bonn.de

Head of CAISA Lab

Vahid Sadiri Javadi

vahidsj(at)bit.uni-bonn.de

Lab Instructor

Coursework:

Assignments (10%):

After completing the first three tutorials you will receive an assignment. The assignment is designed to test your understanding of the concepts covered in the tutorials and to provide you with an opportunity to apply these concepts in a practical context.

  • Deadline: The assignment is due on <DATE> at <TIME>.
  • Submission: The assignment should be submitted via eCampus. Further instructions are given in the assignment file. Please do not email us your assignment.
  • Collaboration: Working on the assignment should be in a group of 2 students. Please name your file with both student names. File name: <FirstName_LastName>.

Final Project (90%):

Project Topics: Each team (a group of two students) chooses one of the following project topics, offered by CAISA researchers:
 

  • <PROJECT TITLE>: <ADVISOR> 
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>
  • <PROJECT TITLE>: <ADVISOR>

Project components:

  • Problem Formulation (PF) (15%)
  • Problem Solving (PS) (40%)
  • Project Presentation (PP) (15%): (12 min. >> 8 - 10 min Presentation + 2 - 4 min Discussion)
    • Intro Presentation
    • Midterm Presentation
    • Final Presentation
  • Project Report (PR) (20%)

Submission: Teams submit each project component on eCampus in the following format:

  • PF: A PDF file with this name: Team_<Team number>.pdf
  • PP: A PDF file with this name: Team_<Team number>.pdf
  • PS + PR: A ZIP file containing all the necessary files with this name: Team_<Team number>.zip
     
  • Deadlines: All deadlines for PF, PP, and PS + PR are listed in the schedule.
  • Mentors: Each team has an advisor, who gives feedback and mentors you during the project.
  • Computing resources:
    • CS Faculty: You can add your Student ID to this <LIST>. GSG will provide you with additional computing resources on behalf of the CAISA lab.
    • Saturn Cloud: You can use 150 hours a month free of 64GB RAM and GPU instances. Check this out.
    • Google Colaboratory: Colab is a hosted Jupyter Notebook service that provides free access to computing resources, including GPUs and TPUs. Check this out.
  • Using external resources: You can use any machine learning or deep learning framework you like (Scikit-learn, Hugging Face, PyTorch, TensorFlow, etc.). You may use any existing code, libraries, etc., and consult papers, books, online references, etc. for your project. However, you must cite your sources in your final project report.
     

Team:

  • Team size: Students should do the final project in teams of 2 persons.
  • Building a team: You can either find your teammate on your own or ask us to find a teammate for you.
  • Submission:

Subject: NLP LAB - SS2024

Team Members: <Name>, <Matr. Nr.>, <Mail Addr.>
                              <Name>, <Matr. Nr.>, <Mail Addr.>

Subject: NLP LAB - SS2024 - Looking for a team

<Name>, <Matr. Nr.>, <Mail Addr.>

  • Deadline: is listed in the schedule.
  • Contribution: In the final report we ask for a statement of what each team member contributed to the project. Team members will typically get the same grade, but we may differentiate in extreme cases of unequal contribution. You can contact us in confidence in the event of unequal contribution.

 

Registration:

Only a limited number (18) of positions are available. Please register for the NLP Lab course until 04.04.2024.

Please send us your registration request via vahid.sadirij(at)uni-bonn.de in the following format:

Subject: NLP LAB - SS2024 - Registration Request
Information: <Name>, <Matr. Nr.>, <Mail Addr.>, <Study Program>

Allocation:

  • Master in Media Informatics: 9 ECTS credits
  • Master in computer science at University of Bonn: MA-INF 4237 9 CP
  • Students must register for the exam on POS/BASIS.

Literature:

  • J. Eisenstein: Introduction to Natural Language Processing
  • Jurafsky, Daniel, and James H. Martin. "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition."
  • S. Bird, E. Klein, E. Loper; Natural Language Processing with Python

Schedule

WeekDateDescriptionEventsDeadlinesAttendance
Week 11st Kick-off Meeting (Wed)

Introductory meeting

  mendatory
Week 2Tutorial #1 (Wed)Tutorial  mendatory
Week 3Tutorial #2 (Wed)Tutorial  mendatory
Week 4Tutorial #3 (Wed)TutorialAssignment OUTTeam Members DUEmendatory
Week 62nd Kick-off Meeting (Wed)Preparatory meeting Assignment DUEmendatory
Week 8Project (Wed)Project PresentationIntro Presentation

Problem Formulation
& Project Presentation DUE

mendatory
Week 8 - 10Project (Wed)Project development  on project team level
Week 11Project (Wed)Project PresentationMidterm PresentationProject Presentation DUEmendatory
Week 12 - 16Project (Wed)Project development  on project team level
Week 17Project (Wed)Project PresentationFinal PresentationProject Presentation DUEmendatory
Week 19Project (Wed)Project development 

Problem Solving
& Project Report DUE

-