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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

Product type Paperback

Published in Mar 2019

Publisher

ISBN-13 9781789954043

Length 374 pages

Edition 1st Edition

Languages

Processing

Tools

NLTK

Concepts

Mobile Application Development

Authors (2):

Dwight Gunning

Sohom Ghosh

View More author details

Table of Contents (10) Chapters

Preface

About the Book

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

Cleaning Text Data

Most of the time, text data cannot be used as it is. This is because the presence of various unknown symbols or links makes it dirty or unfit for use. Data cleaning is the art of extracting meaningful portions from data by eliminating unnecessary details. Consider the sentence, He tweeted, 'Live coverage of General Elections available at this.tv/show/ge2019. _/\_ Please tune in :) '.

Various symbols, such as "_/\_" and ":)," are present in the sentence. They do not contribute much to its meaning. We need to remove such unwanted details. This is done not only to focus more on the actual content but also to reduce computations. To achieve this, methods such as tokenization and stemming are used. We will learn about them one by one in the upcoming sections.

Tokenization

Tokenization and word tokenizers were briefly described in Chapter 1, Introduction to Natural Language Processing. Tokenization is the process of splitting sentences...

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Authors (2)

Sohom Ghosh

Sohom Ghosh

Sohom Ghosh is a passionate data detective with expertise in natural language processing. He has worked extensively in the data science arena with a specialization in deep learning-based text analytics, NLP, and recommendation systems. He has publications in several international conferences and journals.

See other products by Sohom Ghosh

Dwight Gunning

Dwight Gunning

Dwight Gunning is a data scientist at FINRA, a financial services regulator in the US. He has extensive experience in Python-based machine learning and hands-on experience with the most popular NLP tools such as NLTK, gensim, and spacy.

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