Hands-On Natural Language Processing with Python

封面

Title Page

Copyright and Credits

Hands-On Natural Language Processing with Python

Packt Upsell

Why subscribe?

PacktPub.com

Foreword

Contributors

About the authors

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Getting Started

Basic concepts and terminologies in NLP

Text corpus or corpora

Paragraph

Sentences

Phrases and words

N-grams

Bag-of-words

Applications of NLP

Analyzing sentiment

Recognizing named entities

Linking entities

Translating text

Natural Language Inference

Semantic Role Labeling

Relation extraction

SQL query generation or semantic parsing

Machine Comprehension

Textual Entailment

Coreference resolution

Searching

Question answering and chatbots

Converting text-to-voice

Converting voice-to-text

Speaker identification

Spoken dialog systems

Other applications

Summary

Text Classification and POS Tagging Using NLTK

Installing NLTK and its modules

Text preprocessing and exploratory analysis

Tokenization

Stemming

Removing stop words

Exploratory analysis of text

POS tagging

What is POS tagging?

Applications of POS tagging

Training a POS tagger

Training a sentiment classifier for movie reviews

Training a bag-of-words classifier

Summary

Deep Learning and TensorFlow

Deep learning

Perceptron

Activation functions

Sigmoid

Hyperbolic tangent

Rectified linear unit

Neural network

One-hot encoding

Softmax

Cross-entropy

Training neural networks

Backpropagation

Gradient descent

Stochastic gradient descent

Regularization techniques

Dropout

Batch normalization

L1 and L2 normalization

Convolutional Neural Network

Kernel

Max pooling

Recurrent neural network

Long-Short Term Memory

TensorFlow

General Purpose – Graphics Processing Unit

CUDA

cuDNN

Installation

Hello world!

Adding two numbers

TensorBoard

The Keras library

Summary

Semantic Embedding Using Shallow Models

Word vectors

The classical approach