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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook - Second Edition

By : Zhenya Antić, Saurabh Chakravarty
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

5 (5)
By: Zhenya Antić, Saurabh Chakravarty

Overview of this book

Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Table of Contents (13 chapters)
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Performing rule-based text classification using keywords

In this recipe, we will use the vocabulary of the text to classify the Rotten Tomatoes reviews. We will create a simple classifier that will have a vectorizer for each class. That vectorizer will include the words characteristic to that class. The classification will simply be vectorizing the text using each of the vectorizers and then using the class that has more words.

Getting ready

We will use the CountVectorizer class and the classification_report function from sklearn, as well as the word_tokenize method from NLTK. All of these are included in the poetry environment.

The notebook is located at https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook-Second-Edition/blob/main/Chapter04/4.2_rule_based.ipynb.

How to do it…

In this recipe, we will create a separate vectorizer for each class. We will then use those vectorizers to count the number of each class word in each review to...

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Python Natural Language Processing Cookbook
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