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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
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Sohom Ghosh
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Basic Feature Extraction Methods 3. Developing a Text classifier 4. Collecting Text Data from the Web 5. Topic Modeling 6. Text Summarization and Text Generation 7. Vector Representation 8. Sentiment Analysis Appendix

Developing a Text Classifier

A text classifier is a machine learning model that is capable of labeling texts based on their content. For instance, a text classifier will help you understand whether a random text statement is sarcastic or not. Presently, text classifiers are gaining importance as manually classifying huge amounts of text data is impossible.

Feature Extraction

When dealing with text data, features denote its different attributes. Generally, they are numeric representations of the text. As discussed in Chapter 2- Extraction Methods from Unstructured text, TF-IDF representations of texts are one of the most popular ways of extracting features from them.

Feature Engineering

Feature engineering is the art of extracting new features from existing ones. Extracting novel features, which tend to capture variation in data better, requires sound domain expertise.

Removing Correlated Features

Regression models, including logistic regression, are unable to perform...

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