Unit 6 Algorithms and libraries

Activity 1 Introduction to NLP libraries

Read about the ecosystem of Python NLP libraries.

Python's strength in NLP comes from its rich ecosystem of specialized libraries. Each library has unique strengths and is optimized for different tasks. Understanding when to use which library is crucial for efficient NLP development.

Major Python NLP libraries include:

• NLTK (Natural Language Toolkit): Comprehensive, educational, great for learning NLP concepts
• spaCy: Industrial-strength, fast, production-ready with pre-trained models
• TextBlob: Simple API, good for beginners, built on NLTK and pattern
• Gensim: Topic modeling and document similarity analysis
• scikit-learn: Machine learning algorithms for text classification and clustering
• Transformers: State-of-the-art pre-trained language models

Activity 2 NLTK fundamentals

Watch this introduction to NLTK library usage.

This video (6 minutes) demonstrates NLTK installation, basic text processing operations, and accessing NLTK's built-in corpora and lexical resources.

Activity 3 Interactive library explorer

Experiment with different NLP libraries and compare their outputs.

Use the tool below to process text with simulated library functions. Compare how different libraries handle tokenization, POS tagging, and named entity recognition.

Activity 4 spaCy for production NLP

Watch how spaCy provides fast, production-ready NLP.

This video (11 minutes) shows spaCy's installation, language models, and how to build efficient NLP pipelines for real-world applications.

Activity 5 Algorithm implementation challenge

Implement classic NLP algorithms from scratch.

Build fundamental NLP algorithms without using library functions. This helps you understand the underlying computational processes before using optimized library implementations.

Activity 6 Text processing pipeline builder

Build a comprehensive text processing pipeline using multiple libraries.

Create a multi-stage pipeline that combines different libraries for preprocessing, analysis, and output formatting. This simulates real-world NLP application development.

Activity 7 Library performance benchmarking

Compare performance characteristics of different NLP libraries.

Analyze how different libraries perform on various tasks in terms of speed, memory usage, and accuracy. This helps you make informed decisions for production systems.