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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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Representing Text – Capturing Semantics

Representing the meaning of words, phrases, and sentences in a form that’s understandable to computers is one of the pillars of NLP processing. Machine learning, for example, represents each data point as a list of numbers (a fixed-size vector), and we are faced with the question of how to turn words and sentences into these vectors. Most NLP tasks start by representing the text in some numeric form, and in this chapter, we show several ways to do that.

First, we will create a simple classifier to demonstrate the effectiveness of each method of encoding, and then we will use it to test the different encoding methods. We will also learn how to turn phrases such as fried chicken into vectors – that is, how to train a word2vec model for phrases. Finally, we will see how to use vector-based search.

For a theoretical background on some of the concepts discussed in this section, refer to Building Machine Learning Systems...

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