Fundamentals of Natural Language Processing (NLP) in Python

Please note that this is a pilot workshop, which means that we are teaching new and exciting lesson material. The additional purpose of pilot workshops is to collect information and feedback that can be used to polish content and make the lesson more reusable by other instructors.

The workshop will take place at Science Park 402, 1098 XH Amsterdam. Please note that lunch and drinks at the end of the workshop are included.

This lesson teaches the fundamentals of Natural Language Processing (NLP) in Python. It will equip you with the foundational skills and knowledge needed to carry over text-based research projects. The lesson is designed with researchers in the Humanities and Social Sciences in mind, but is also applicable to other fields of research.

On the first day we will dive in to text preprocessing and word embeddings while epxloring semantic shifts in various words over multiple decades. The second day begins with an introduction to transformers, and we will work on classification and named entity recognition with the BERT model. In the afternoon, we willl cover large language language models, and you will learn how to build your own agents.

The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the workshop website (will be activated once registration is live).

Cancellation

If you won’t be able to attend, please cancel your registration (by following instructions here) so that we can offer your seat to someone on the waiting list.

Audience

The workshop is aimed at PhD candidates, postdocs, (assistant-)professors and other researchers and also open for research support staff and research software engineers. We offer tickets for researchers who are affiliated with Dutch research institutions. We do not accept registrations by Master students.

Prerequisites

The participant should:

• be familiar with Python
• be comfortable working in Jupyter

Syllabus

Introduction

• What is NLP?
• Core applications
• NLP tasks
• Natural vs Artificial Language

From text to vectors

• Formulate the problem: semantic shift
• Download and inspect the data
• Prepare data to be ingested by the model (preprocessing)
• Tracing semantic shifts with word embeddings
• Train the Word2Vec model
• Load the embeddings and inspect them

BERT and Transformers

• Transformers
• BERT
• BERT Architecture
• BERT as a Language Model
• BERT for Text Classification
• Understanding BERT Architecture
• BERT for Token Classification

Large Language Models

• What are Large Language Models?
• Building a chatbot
• Retrieval Augmented Generation – Build a RAG