Original article was published by Alex Moltzau on Artificial Intelligence on Medium
Books about Python and NLP
Finding Books about Natural Language Processing
I thought I would spend a short moment to search for book and gather the descriptions of a few books on the topic of Python and different libraries.
So, that is I thought to make a list of books I am considering to acquire. If you have read any of these please reach out! I have the first book in my library. This list is in no specific order.
Natural Language Processing with Python (NLTK) (2009)
I was sent this book about Natural Language Processing with Python that I assume is an excellent book from 2009. Being from 2009 does by no means make it a bad book, quite the contrary, I am sure there is much insight and interesting thoughts that have appeared in the original and the revisions. However, I found that I wanted to be working with spaCy as a package as opposed to NLTK that is represented in the book. It has an introduction to natural language processing (NLP), however it begins from an NLTK standpoint from the first section.
It has quite good reviews, but only two of those are from 2020.
Natural Language Processing with Python and spaCy (2020)
“Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You’ll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You’ll even learn how to transform statements into questions to keep a conversation going.”
- Chapter 1: How Natural Language Processing Works
- Chapter 2: The Text-Processing Pipeline
- Chapter 3: Working with Container Objects and Customizing spaCy
- Chapter 4: Extracting and Using Linguistic Features
- Chapter 5: Working with Word Vectors
- Chapter 6: Finding Patterns and Walking Dependency Trees
- Chapter 7: Visualizations
- Chapter 8: Intent Recognition
- Chapter 9: Storing User Input in a Database
- Chapter 10: Training Models
- Chapter 11: Deploying Your Own Chatbot
- Chapter 12: Implementing Web Data and Processing Images Linguistic Primer
Downside: it only has one review on Amazon and it is one star.
Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems (2020)
“Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail.”
With this book, you’ll:
- Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP
- Implement and evaluate different NLP applications using machine learning and deep learning methods
- Fine-tune your NLP solution based on your business problem and industry vertical
- Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages
- Produce software solutions following best practices around release, deployment, and DevOps for NLP systems
- Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Natural Language Processing in Action: Understanding, analyzing, and generating text with Python (2019)
“Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.
- Some sentences in this book were written by NLP! Can you guess which ones?
- Working with Keras, TensorFlow, gensim, and scikit-learn
- Rule-based and data-based NLP
- Scalable pipelines”