Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
4 (1)
Book Image

Hands-On Python Natural Language Processing

4 (1)
By: Aman Kedia, Mayank Rasu

Overview of this book

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Introduction
Section 1: Introduction
Free Chapter
2
Understanding the Basics of NLP
Understanding the Basics of NLP
Programming languages versus natural languages
Why should I learn NLP?
Current applications of NLP
Summary
3
NLP Using Python
NLP Using Python
Technical requirements
Understanding Python with NLP
Important Python libraries
Web scraping libraries and methodology
Overview of Jupyter Notebook
Summary
4
Section 2: Natural Language Representation and Mathematics
Section 2: Natural Language Representation and Mathematics
5
Building Your NLP Vocabulary
Building Your NLP Vocabulary
Technical requirements
Lexicons
Phonemes, graphemes, and morphemes
Tokenization
Understanding word normalization
Summary
6
Transforming Text into Data Structures
Transforming Text into Data Structures
Technical requirements
Understanding vectors and matrices
Exploring the Bag-of-Words architecture
TF-IDF vectors
Distance/similarity calculation between document vectors
One-hot vectorization
Building a basic chatbot
Summary
7
Word Embeddings and Distance Measurements for Text
Word Embeddings and Distance Measurements for Text
Technical requirements
Understanding word embeddings
Demystifying Word2vec
Training a Word2vec model
Word mover’s distance
Summary
8
Exploring Sentence-, Document-, and Character-Level Embeddings
Exploring Sentence-, Document-, and Character-Level Embeddings
Technical requirements
Venturing into Doc2Vec
Exploring fastText
Understanding Sent2Vec and the Universal Sentence Encoder</span>
Summary
9
Section 3: NLP and Learning
Section 3: NLP and Learning
10
Identifying Patterns in Text Using Machine Learning
Identifying Patterns in Text Using Machine Learning
Technical requirements
Introduction to ML
Data preprocessing
The Naive Bayes algorithm
The SVM algorithm
Productionizing a trained sentiment analyzer
Summary
11
From Human Neurons to Artificial Neurons for Understanding Text
From Human Neurons to Artificial Neurons for Understanding Text
Technical requirements
Exploring the biology behind neural networks
How does a neural network learn?
Understanding regularization
Let's talk Keras
Building a question classifier using neural networks
Summary
12
Applying Convolutions to Text
Applying Convolutions to Text
Technical requirements
What is a CNN?
Detecting sarcasm in text using CNNs
Summary
13
Capturing Temporal Relationships in Text
Capturing Temporal Relationships in Text
Technical requirements
Baby steps toward understanding RNNs
Vanishing and exploding gradients
Architectural forms of RNNs
Giving memory to our networks – LSTMs
Building a text generator using LSTMs
Exploring memory-based variants of the RNN architecture
Summary
14
State of the Art in NLP
State of the Art in NLP
Technical requirements
Seq2Seq modeling
Translating between languages using Seq2Seq modeling
Let's pay some attention
Transformers
BERT
Summary
15
Other Books You May Enjoy
Other Books You May Enjoy
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The Naive Bayes algorithm

In this section, we will delve into the Naive Bayes algorithm and build a sentiment analyzer. Naive Bayes is a popular ML algorithm based on the Bayes' theorem. The Bayes' theorem can be represented as follows:

Here, A, B are events:

  • P(A|B) is the probability of A given B, while P(B|A) is the probability of B given A.
  • P(A) is the independent probability of A, while P(B) is the independent probability of B.

Let's say we have the following fictitious dataset containing information about applications to Ivy League schools. The independent variables in the dataset are the applicant's SAT score, applicant's GPA, and information regarding whether the applicant's parents are alumni to an Ivy League school. The dependent variable is the outcome of the application. Based on this data, we are interested in calculating the likelihood of an applicant getting admission to an Ivy League school given that their SAT score is greater than 1...

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