Introduction to Natural Language Processing in Python

Please visit Language and Data Intelligence Lab for more information.

This tutorial is largely based on fastai's excellent NLP Lecture Series.

The course is taught in Python with Google Colab Notebooks, using libraries such as sklearn, nltk, pytorch, and fastai.

For any questions or comments, please contact [email protected].

Table of Contents

First, add the following Google Drive folder to your own Google Drive: Preprocessed Data.

Next, we'll visit each of the links below to run the tutorial codes in Google Colab.

We'll cover the following topics:

Part 1. Introduction to NLP: Sentiment Classification

• Sentiment Classification without Deep Learning
• Data handling for NLP
• Dictionary, Vocabulary, Bag-of-words
• Naive Bayes classifier

Part 2. Introduction to NLP with Deep Learning: Machine Translation

• RNN
• GRU
• Seq2Seq for French to English translation
• Attention

Part 3. Introduction to Transformers

• Transformers
• Positional Embeddings
• Self-attention
• Layernorm

Part 4. Introduction to BERT Finetuning

• BERT
• Finetuning
• GLUE task