NLP_Quickbook Project Introduction
The NLP_Quickbook project is a comprehensive resource for engineers and practitioners who want to dive into Natural Language Processing (NLP). It is based on existing foundational works in the field from classic to modern resources, such as Jurafsky's "Speech and Language Processing" and Ian Goodfellow's "The Deep Learning Book". Unlike traditional textbooks, this project is structured to allow users to quickly find and apply useful concepts. It includes several notebooks, organized into seven thematic sections, that provide a practical and hands-on approach to NLP.
Chapter 01: Introduction to Text Processing, with Text Classification
This chapter serves as a perfect starting point for beginners. It emphasizes learning through a code-first approach, allowing users to grasp text processing concepts effectively and quickly.
Chapter 02: Text Cleaning and Spell Correction
The chapter is divided into two parts:
- Text Cleaning: This section offers a code-first approach with detailed explanations. Key topics include removing stop words and lemmatization, essential first steps in cleaning data.
- Spell Correction: This notebook covers almost every aspect needed to start with spelling correction and dealing with similar words challenges.
Chapter 03: Leveraging Linguistics
Utilizing tools like spaCy and textacy, this chapter covers advanced topics such as:
- Named Entity Recognition for identifying and redacting names in text.
- Question and Answer Generation using Part of Speech Tagging and Dependency Parsing.
Chapter 04: Text Representations
This section delves into transforming text into numeric vectors using models such as word2vec, fasttext, and doc2vec, facilitating document similarity analysis. It also includes a programmer's guide to gensim, a popular Python library for topic modeling.
Chapter 05: Modern Methods for Text Classification
Focusing on modern, practical methodologies, this chapter explores:
- Simple classifiers and optimization techniques from scikit-learn.
- Combining models through ensemble techniques to enhance performance.
- Building intuition to develop personalized ensemble methods.
Chapter 06: Deep Learning for NLP
This chapter emphasizes the engineering aspects of deep learning over complex data modeling. Key focus areas include:
- From scratch code tutorials for text classification.
- Using tools such as PyTorch and torchtext for hands-on learning.
- Building custom data loaders, preprocessing scripts, training loops, and utility functions.
Chapter 07: Building Your Own Chatbot
The final chapter guides users in creating a chatbot from scratch in just 30 minutes. This exercise in unsupervised learning brings together various concepts covered earlier:
- Simplified, direct problem formulation contrasting typical complex tutorials.
- Understanding chatbot intents, responses, and templates.
- Innovatively using a word-based similarity engine with minimal training data.
The NLP_Quickbook project is an invaluable toolkit for engineers looking to get practical, code-oriented experience with NLP, leading from basic text processing to modern deep learning techniques and real-world applications like developing chatbots.