Natural Language Processing Fundamentals
Chapter 1: NLP Concepts and Advanced Text Processing
The Natural Language Processing Pipeline
Advanced Tokenization Methods
Stemming and Lemmatization Compared
Handling Noise in Text Data
Advanced Stop Word Customization
Text Normalization Techniques
Hands-on Practical: Building Preprocessing Pipelines
Quiz for Chapter 1
Chapter 2: Feature Engineering for Text Representation
From Bag-of-Words to TF-IDF
Calculating TF-IDF Scores
Using N-grams to Capture Context
Introduction to Feature Hashing
Dimensionality Reduction for Text Features
Comparing Different Text Representation Methods
Hands-on Practical: Generating Text Features
Quiz for Chapter 2
Chapter 3: Supervised Learning for Text Classification
Classification Algorithms Review
Applying Classifiers to Text Data
Model Evaluation Metrics for Classification
Cross-Validation Strategies
Hyperparameter Tuning for Text Models
Addressing Imbalanced Datasets
Practice: Building a Text Classifier
Quiz for Chapter 3
Chapter 4: Understanding Sequential Data with Embeddings
Limitations of Frequency-Based Models
Introduction to Distributional Semantics
Word Embedding Concepts
Word2Vec: CBOW and Skip-gram Architectures
GloVe: Global Vectors for Word Representation
Visualizing Word Embeddings
Using Pre-trained Word Embedding Models
Hands-on Practical: Working with Word Embeddings
Quiz for Chapter 4
Chapter 5: Introduction to Sequence Models for NLP
The Need for Sequence Awareness
Recurrent Neural Network (RNN) Basics
Understanding the Vanishing Gradient Problem
Long Short-Term Memory (LSTM) Networks
Gated Recurrent Units (GRUs)
Applying Sequence Models to Text
Hands-on Practical: Building a Simple Sequence Model
Quiz for Chapter 5
Using Pre-trained Word Embedding Models
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- Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, 2013 arXiv preprint arXiv:1301.3781 DOI: 10.48550/arXiv.1301.3781 - Introduces the original Word2Vec model and its two architectures (CBOW and Skip-gram) for learning efficient word representations.
- GloVe: Global Vectors for Word Representation, Jeffrey Pennington, Richard Socher, Christopher Manning, 2014 Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics) DOI: 10.3115/v1/D14-1162 - Presents the GloVe model, which combines global matrix factorization and local context window methods for word representation learning.
- Enriching Word Vectors with Subword Information, Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017 Transactions of the Association for Computational Linguistics (TACL), Vol. 5 (MIT Press) DOI: 10.1162/tacl_a_00051 - Describes fastText, an extension of Word2Vec that incorporates subword (character n-gram) information, allowing for better handling of rare and out-of-vocabulary words.
- Speech and Language Processing (3rd ed. draft), Daniel Jurafsky, James H. Martin, 2025 (Stanford University) - A comprehensive textbook covering various NLP topics, with detailed chapters on word embeddings, their history, and applications. Specifically relevant is Chapter 6, "Vector Semantics and Embeddings".