Natural Language Processing for the Working Programmer
✒️ By Daniël de Kok, Harm Brouwer
Natural Language Processing for the Working Programmer is a hands-on introduction to NLP concepts and techniques, focusing on practical applications using Haskell. Written by Daniël de Kok and Harm Brouwer, this book demystifies complex topics like tokenization, n-grams, classification, and part-of-speech tagging. It’s perfect for programmers, computational linguists, and tech enthusiasts eager to dive into language processing with a functional programming twist.
Book Description
Natural Language Processing for the Working Programmer is your gateway to understanding how computers analyze human language. Authored by Daniël de Kok and Harm Brouwer, this book brings clarity to complex NLP topics with a practical, programmer-friendly approach. Whether you’re a developer curious about text analysis or a computational linguist seeking new tools, this guide offers actionable insights into real-world language processing.
The book stands out by focusing on Haskella functional programming language known for its purity and expressiveness. If you’ve ever wondered how to process text data efficiently or wanted to explore the structure behind natural language, this book is your friendly companion. The writing style is approachable, and the examples are grounded in everyday programming challenges.
You’ll find chapters covering everything from word tokenization to advanced classification methods like Naive Bayes and Maximum Entropy. The authors don’t just skim the surface; they provide code snippets, exercises, and clear explanations that make even tricky concepts accessible. No prior NLP experience? No problem! The book starts from the basics and builds up your knowledge step by step.
This resource is ideal for:
- Software engineers wanting to add NLP skills to their toolkit
- Students in computational linguistics or computer science
- Data scientists exploring text analytics
- Haskell enthusiasts looking for real-world projects
- Anyone curious about how machines understand language
What You Will Learn
- The fundamentals of natural language processing (NLP)
- How to use Haskell for text analysis tasks
- Techniques for tokenizing words and sentences
- Building n-gram models, including bigrams and suffix arrays
- Implementing word frequency lists and monads in practice
- Classifying text with Naive Bayes and Maximum Entropy models
- Approaches to part-of-speech tagging and evaluation methods
- An introduction to context-free grammars and regular languages (proposed sections)
- Performance optimization tips for large-scale text processing
If you’re interested in expanding your knowledge further, check out how other languages handle text analysis in Text Processing in Python by David Mertz. For those curious about logic-based approaches to language understanding, see Prolog and Natural Language Analysis pdf.
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