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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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Topic Modeling

In this chapter, we will cover topic modeling, or the classification of topics present in a corpus of text. Topic modeling is a very useful technique that can give us an idea about which topics appear in a document set. For example, topic modeling is used for trend discovery on social media. Also, in many cases, it is useful to do topic modeling as part of the preliminary data analysis of a dataset to understand which topics appear in it.

There are many different algorithms available to do this. All of them try to find similarities between different texts and put them into several clusters. These different clusters indicate different topics.

You will learn how to create and use topic models via various techniques with the BBC news dataset in this chapter. This dataset has news that falls within the following topics: politics, sport, business, tech, and entertainment. Thus, we know that in each case, we need to have five topic clusters. This is not going to be the...

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Python Natural Language Processing Cookbook
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