Book Image

Python Natural Language Processing

Book Image

Python Natural Language Processing

Overview of this book

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.

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Table of Contents (13 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Introduction
Introduction
Understanding natural language processing
Understanding basic applications
Advantages of togetherness - NLP and Python
Environment setup for NLTK
Tips for readers
Summary
2
Practical Understanding of a Corpus and Dataset
Practical Understanding of a Corpus and Dataset
What is a corpus?
Why do we need a corpus?
Understanding corpus analysis
Understanding types of data attributes
Exploring different file formats for corpora
Resources for accessing free corpora
Preparing a dataset for NLP applications
Web scraping
Summary
3
Understanding the Structure of a Sentences
Understanding the Structure of a Sentences
Understanding components of NLP
Natural language understanding
Defining context-free grammar
Morphological analysis
Syntactic analysis
Semantic analysis
Handling ambiguity
Discourse integration
Pragmatic analysis
Summary
4
Preprocessing
Preprocessing
Handling corpus-raw text
Handling corpus-raw sentences
Basic preprocessing
Practical and customized preprocessing
Summary
5
Feature Engineering and NLP Algorithms
Feature Engineering and NLP Algorithms
Understanding feature engineering
Basic feature of NLP
Basic statistical features for NLP
Advantages of features engineering
Challenges of features engineering
Summary
6
Advanced Feature Engineering and NLP Algorithms
Advanced Feature Engineering and NLP Algorithms
Recall word embedding
Understanding the basics of word2vec
Converting the word2vec model from black box to white box
Understanding the components of the word2vec model
Understanding the logic of the word2vec model
Understanding algorithmic techniques and the mathematics behind the word2vec model
Algorithms used by neural networks
Some of the facts related to word2vec
Applications of word2vec
Implementation of simple examples
Advantages of word2vec
Challenges of word2vec
How is word2vec used in real-life applications?
When should you use word2vec?
Developing something interesting
Extension of the word2vec concept
Importance of vectorization in deep learning
Summary
7
Rule-Based System for NLP
Rule-Based System for NLP
Understanding of the rule-based system
Purpose of having the rule-based system
Architecture of the RB system
Understanding the RB system development life cycle
Applications
Developing NLP applications using the RB system
Comparing the rule-based approach with other approaches
Advantages of the rule-based system
Disadvantages of the rule-based system
Challenges for the rule-based system
Understanding word-sense disambiguation basics
Discussing recent trends for the rule-based system
Summary
8
Machine Learning for NLP Problems
Machine Learning for NLP Problems
Understanding the basics of machine learning
Development steps for NLP applications
Understanding ML algorithms and other concepts
Hybrid approaches for NLP applications
Summary
9
Deep Learning for NLU and NLG Problems
Deep Learning for NLU and NLG Problems
An overview of artificial intelligence
Comparing NLU and NLG
A brief overview of deep learning
Basics of neural networks
Implementation of ANN
Deep learning and deep neural networks
Deep learning techniques and NLU
Deep learning techniques and NLG
Gradient descent-based optimization
Artificial intelligence versus human intelligence
Summary
10
Advanced Tools
Advanced Tools
Apache Hadoop as a storage framework
Apache Spark as a processing framework
Apache Flink as a real-time processing framework
Visualization libraries in Python
Summary
11
How to Improve Your NLP Skills
How to Improve Your NLP Skills
Beginning a new career journey with NLP
Cheat sheets
Choose your area
Agile way of working to achieve success
Useful blogs for NLP and data science
Grab public datasets
Mathematics needed for data science
Summary
12
Installation Guide
Installation Guide
Installing Python, pip, and NLTK
Installing the PyCharm IDE
Installing dependencies
Framework installation guides
Drop your queries
Summary

Understanding basic applications

NLP is a sub-branch of AI. Concepts from NLP are used in the following expert systems:

  • Speech recognition system
  • Question answering system
  • Translation from one specific language to another specific language
  • Text summarization
  • Sentiment analysis
  • Template-based chatbots
  • Text classification
  • Topic segmentation

We will learn about most of the NLP concepts that are used in the preceding applications in the further chapters.

Understanding advanced applications

Advanced applications include the following:

  • Human robots who understand natural language commands and interact with humans in natural language.
  • Building a universal machine translation system is the long-term goal in the NLP domain because you could easily build a machine translation system which can convert one specific language to another specific language, but that system may not help you to translate other languages. With the help of deep learning, we can develop a universal machine translation system and Google recently announced that they are very close to achieving this goal. We will build our own machine translation system using deep learning in Chapter 9, Deep Learning for NLP and NLG Problems.
  • The NLP system, which generates the logical title for the given document is one of the advance applications. Also, with the help of deep learning, you can generate the title of document and perform summarization on top of that. This kind of application, you will see in Chapter 9, Deep Learning for NLP and NLG Problems.
  • The NLP system, which generates text for specific topics or for an image is also considered an advanced NLP application.
  • Advanced chatbots, which generate personalized text for humans and ignore mistakes in human writing is also a goal we are trying to achieve.
  • There are many other NLP applications, which you can see in Figure 1.5:
Figure 1.5: Applications In NLP domain
Previous Section
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