Python Natural Language Processing by Jacob Perkins

Python Natural Language Processing (NLP) by Jacob Perkins is a comprehensive and practical guide to building real-world NLP systems. 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. Perkins 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. Perkins 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, Python Natural Language Processing is an excellent book for anyone who wants to learn how to build NLP systems in Python. It is well-written, informative, and practical.

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.

Perkins 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, Perkins 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.

Long expand about the book:

Python Natural Language Processing is a comprehensive and practical guide to building real-world NLP systems. The book covers a wide range of topics, including:

• Text processing and tokenization: This chapter covers the basics of text processing, such as cleaning text data and removing stop words. It also covers how to tokenize text, which is the process of breaking a sentence down into its individual words.
• Part-of-speech tagging and parsing: This chapter covers how to use NLTK to perform part-of-speech tagging and parsing. Part-of-speech tagging is the task of assigning a part-of-speech tag to each word in a sentence. Parsing is the task of constructing a parse tree for a sentence, which shows the grammatical relationships between the words in the sentence.
• Named entity recognition: This chapter covers 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.
• Machine translation: This chapter covers how to use NLTK to build a simple machine translation system. Machine translation is the task of translating a text from one language to another.
• Text summarization: This chapter covers how to use NLTK to summarize text. Text summarization is the task of creating a condensed version of a text that preserves the most important information.
• Sentiment analysis: This chapter covers how to use NLTK to perform sentiment analysis. Sentiment analysis is the task of identifying the sentiment of a text, such as whether it is positive, negative, or neutral.
• Information retrieval: This chapter covers how to use NLTK to build a simple information retrieval system. Information retrieval is the task of finding relevant documents in a collection of documents based on a user query.

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, Python Natural Language Processing is an excellent book for anyone who wants to learn how to build NLP systems in Python. It is well-written, informative, and practical.