Estimated reading time: 4 minutes
Natural Language Processing in Action by Hannes Hapke
Natural Language Processing in Action is a comprehensive and practical guide to building real-world NLP systems using Python. The book covers a wide range of topics, including:
- Text processing and tokenization
- Part-of-speech tagging and parsing
- Named entity recognition
- Machine translation
- Text summarization
- Sentiment analysis
- Information retrieval
Each chapter includes hands-on exercises and code examples, making the book ideal for self-study or classroom use.
The book is well-written and easy to follow, even for readers with no prior experience in NLP. Hapke does a good job of explaining complex concepts in a clear and concise way. He also provides helpful tips and tricks for working with Python NLP libraries, such as NLTK and spaCy.
One of the things that makes this book so valuable is its focus on practical applications. Hapke shows readers how to use NLP techniques to solve real-world problems, such as:
- Building a spam classifier
- Developing a chat bot
- Translating a document from one language to another
- Summarizing a news article
- Analyzing customer reviews
The book also includes a number of case studies, which show how NLP is being used in the real world by companies such as Google, Amazon, and Microsoft.
Overall, Natural Language Processing in Action is an excellent book for anyone who wants to learn how to build NLP systems in Python. It is well-written, informative, and practical.
Comparison with Python Natural Language Processing:
Both Python Natural Language Processing and Natural Language Processing in Action are excellent books for learning how to build NLP systems in Python. However, there are a few key differences between the two books.
Python Natural Language Processing is a more comprehensive book, covering a wider range of topics. It also includes more case studies and code examples. Natural Language Processing in Action, on the other hand, is more focused on the practical applications of NLP. It also introduces some more advanced topics, such as deep learning for NLP.
If you are new to NLP, I recommend starting with Python Natural Language Processing. It is a great introduction to the field, and it will give you a solid foundation in the basics of NLP. Once you have a good understanding of the basics, you can then move on to Natural Language Processing in Action to learn about more advanced topics and practical applications.
Examples from the book:
One of the first examples in the book is how to use NLTK to tokenize a sentence. Tokenization is the process of breaking a sentence down into its individual words. This is an important step in many NLP tasks, such as part-of-speech tagging and parsing.
Another example from the book is how to use spaCy to perform named entity recognition (NER). NER is the task of identifying and classifying named entities in a text, such as people, places, and organizations.
Hapke also shows readers how to use NLTK to build a simple spam classifier. This is a classic NLP task that can be used to filter out spam emails.
Finally, Hapke shows readers how to use spaCy to translate a document from one language to another. This is a powerful NLP task that can be used to communicate with people from all over the world.
Conclusion:
Both Python Natural Language Processing and Natural Language Processing in Action are excellent books for learning how to build NLP systems in Python. If you are new to NLP, I recommend starting with Python Natural Language Processing. If you have a good understanding of the basics, you can then move on to Natural Language Processing in Action to learn about more advanced topics and practical applications.