Book description
Gain knowledge of various deep neural network architectures and their areas of application to conquer your NLP issues
Key Features
- Gain insights into the basic building blocks of natural language processing
- Learn how to select the best deep neural network to solve your NLP problems
- Explore convolutional and recurrent neural networks and long short-term memory networks
Book Description
Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts by highlighting the basic building blocks of the natural language processing domain.
The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
By the end of this book, you will not only have sound knowledge of natural language processing, but also be able to select the best text preprocessing and neural network models to solve a number of NLP issues.
What you will learn
- Understand various preprocessing techniques for solving deep learning problems
- Build a vector representation of text using word2vec and GloVe
- Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
- Build a machine translation model in Keras
- Develop a text generation application using LSTM
- Build a trigger word detection application using an attention model
Who this book is for
If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Table of contents
- Preface
- Chapter 1
-
Introduction to Natural Language Processing
- Introduction
- The Basics of Natural Language Processing
- Capabilities of Natural language processing
-
Applications of Natural Language Processing
- Text Preprocessing
- Text Preprocessing Techniques
- Lowercasing/Uppercasing
- Exercise 1: Performing Lowercasing on a Sentence
- Noise Removal
- Exercise 2: Removing Noise from Words
- Text Normalization
- Stemming
- Exercise 3: Performing Stemming on Words
- Lemmatization
- Exercise 4: Performing Lemmatization on Words
- Tokenization
- Exercise 5: Tokenizing Words
- Exercise 6: Tokenizing Sentences
- Additional Techniques
- Exercise 7: Removing Stop Words
- Word Embeddings
- Summary
- Chapter 2
-
Applications of Natural Language Processing
- Introduction
- POS Tagging
- Applications of Parts of Speech Tagging
- Chunking
- Chinking
-
Named Entity Recognition
- Named Entities
- Named Entity Recognizers
- Applications of Named Entity Recognition
- Types of Named Entity Recognizers
- Rule-Based NERs
- Stochastic NERs
- Exercise 15: Perform Named Entity Recognition with NLTK
- Exercise 16: Performing Named Entity Recognition with spaCy
- Activity 3: Performing NER on a Tagged Corpus
- Summary
- Chapter 3
- Introduction to Neural Networks
- Chapter 4
- Foundations of Convolutional Neural Network
- Chapter 5
-
Recurrent Neural Networks
- Introduction
- Previous Versions of Neural Networks
- RNNs
- Updates and Gradient Flow
-
Gradients
- Exploding Gradients
- Vanishing Gradients
- RNNs with Keras
- Exercise 23: Building an RNN Model to Show the Stability of Parameters over Time
- Stateful versus Stateless
- Exercise 24: Turning a Stateless Network into a Stateful Network by Only Changing Arguments
- Activity 6: Solving a Problem with an RNN – Author Attribution
- Summary
- Chapter 6
-
Gated Recurrent Units (GRUs)
- Introduction
- The Drawback of Simple RNNs
- Gated Recurrent Units (GRUs)
-
Sentiment Analysis with GRU
- Exercise 25: Calculating the Model Validation Accuracy and Loss for Sentiment Classification
- Activity 7: Developing a Sentiment Classification Model Using a Simple RNN
- Text Generation with GRUs
- Exercise 26: Generating Text Using GRUs
- Activity 8: Train Your Own Character Generation Model Using a Dataset of Your Choice
- Summary
- Chapter 7
- Long Short-Term Memory (LSTM)
- Chapter 8
- State-of-the-Art Natural Language Processing
- Chapter 9
- A Practical NLP Project Workflow in an Organization
-
Appendix
- Chapter 1: Introduction to Natural Language Processing
- Chapter 2: Applications of Natural Language Processing
- Chapter 3: Introduction to Neural Networks
- Chapter 4: Introduction to convolutional networks
- Chapter 5: Foundations of Recurrent Neural Network
- Chapter 6: Foundations of GRUs
- Chapter 7: Foundations of LSTM
- Chapter 8: State of the art in Natural Language Processing
- Chapter 9: A practical NLP project workflow in an organisation
Product information
- Title: Deep Learning for Natural Language Processing
- Author(s):
- Release date: June 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838550295
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