Hands-On Python Natural Language Processing by Aman Kedia, Mayank Rasu -- Read -- Imperial Library of Trantor

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Hands-On Python Natural Language Processing

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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

Section 1: Introduction Understanding the Basics of NLP

Programming languages versus natural languages Chatbots Sentiment analysis Machine translation Named-entity recognition Summary

NLP Using Python

Technical requirements Understanding Python with NLP Important Python libraries NLTK corpora Text processing Part of speech tagging Sentiment analysis Machine translation Part of speech tagging VADER Web scraping libraries and methodology Summary

Section 2: Natural Language Representation and Mathematics Building Your NLP Vocabulary

Technical requirements Stemming

Transforming Text into Data Structures

Technical requirements

Word Embeddings and Distance Measurements for Text

Technical requirements Exploring the components of a Skip-gram model v Output vector

Exploring Sentence-, Document-, and Character-Level Embeddings

Technical requirements Building a Doc2Vec model Building a fastText model Building a spelling corrector/word suggestion module using fastText Sent2Vec

Section 3: NLP and Learning Identifying Patterns in Text Using Machine Learning

Technical requirements Min-max standardization Z-score standardization

From Human Neurons to Artificial Neurons for Understanding Text

Technical requirements Exploring the biology behind neural networks Neurons Activation functions Sigmoid Tanh activation Rectified linear unit Layers in an ANN Dropout Let's talk Keras Summary

Applying Convolutions to Text

Technical requirements What is a CNN? Understanding convolutions Let's pad our data Understanding strides in a CNN What is pooling? The fully connected layer Detecting sarcasm in text using CNNs Loading the libraries and the dataset Performing basic data analysis and preprocessing our data Loading the Word2Vec model and vectorizing our data Splitting our dataset into train and test sets Building the model Evaluating and saving our model Summary

Capturing Temporal Relationships in Text

Technical requirements Baby steps toward understanding RNNs Forward propagation in an RNN Backpropagation through time in an RNN Vanishing and exploding gradients Architectural forms of RNNs Different flavors of RNN Carrying relationships both ways using bidirectional RNNs Going deep with RNNs Giving memory to our networks – LSTMs Understanding an LSTM cell Forget gate Input gate Output gate Backpropagation through time in LSTMs Building a text generator using LSTMs Exploring memory-based variants of the RNN architecture GRUs Stacked LSTMs Summary

State of the Art in NLP

Technical requirements Transformers Summary

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