Mastering Natural Language Processing with Python

Maximize your NLP capabilities while creating amazing NLP projects in Python
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Mastering Natural Language Processing with Python

Deepti Chopra, Nisheeth Joshi, Iti Mathur

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Maximize your NLP capabilities while creating amazing NLP projects in Python

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Book Details

ISBN 139781783989041
Paperback238 pages

Book Description

Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.

This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.

You will sequentially be guided through applying machine learning tools to develop various models. We’ll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.

Table of Contents

Chapter 1: Working with Strings
Tokenization
Normalization
Substituting and correcting tokens
Applying Zipf's law to text
Similarity measures
Summary
Chapter 2: Statistical Language Modeling
Understanding word frequency
Applying smoothing on the MLE model
Develop a back-off mechanism for MLE
Applying interpolation on data to get mix and match
Evaluate a language model through perplexity
Applying metropolis hastings in modeling languages
Applying Gibbs sampling in language processing
Summary
Chapter 3: Morphology – Getting Our Feet Wet
Introducing morphology
Understanding stemmer
Understanding lemmatization
Developing a stemmer for non-English language
Morphological analyzer
Morphological generator
Search engine
Summary
Chapter 4: Parts-of-Speech Tagging – Identifying Words
Introducing parts-of-speech tagging
Creating POS-tagged corpora
Selecting a machine learning algorithm
Statistical modeling involving the n-gram approach
Developing a chunker using pos-tagged corpora
Summary
Chapter 5: Parsing – Analyzing Training Data
Introducing parsing
Treebank construction
Extracting Context Free Grammar (CFG) rules from Treebank
Creating a probabilistic Context Free Grammar from CFG
CYK chart parsing algorithm
Earley chart parsing algorithm
Summary
Chapter 6: Semantic Analysis – Meaning Matters
Introducing semantic analysis
Generation of the synset id from Wordnet
Disambiguating senses using Wordnet
Summary
Chapter 7: Sentiment Analysis – I Am Happy
Introducing sentiment analysis
Summary
Chapter 8: Information Retrieval – Accessing Information
Introducing information retrieval
Vector space scoring and query operator interaction
Developing an IR system using latent semantic indexing
Text summarization
Question-answering system
Summary
Chapter 9: Discourse Analysis – Knowing Is Believing
Introducing discourse analysis
Summary
Chapter 10: Evaluation of NLP Systems – Analyzing Performance
The need for evaluation of NLP systems
Evaluation of IR system
Metrics for error identification
Metrics based on lexical matching
Metrics based on syntactic matching
Metrics using shallow semantic matching
Summary

What You Will Learn

  • Implement string matching algorithms and normalization techniques
  • Implement statistical language modeling techniques
  • Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
  • Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
  • Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
  • Develop an NER-based system and understand and apply the concepts of sentiment analysis
  • Understand and implement the concepts of Information Retrieval and text summarization
  • Develop a Discourse Analysis System and Anaphora Resolution based system

Authors

Table of Contents

Chapter 1: Working with Strings
Tokenization
Normalization
Substituting and correcting tokens
Applying Zipf's law to text
Similarity measures
Summary
Chapter 2: Statistical Language Modeling
Understanding word frequency
Applying smoothing on the MLE model
Develop a back-off mechanism for MLE
Applying interpolation on data to get mix and match
Evaluate a language model through perplexity
Applying metropolis hastings in modeling languages
Applying Gibbs sampling in language processing
Summary
Chapter 3: Morphology – Getting Our Feet Wet
Introducing morphology
Understanding stemmer
Understanding lemmatization
Developing a stemmer for non-English language
Morphological analyzer
Morphological generator
Search engine
Summary
Chapter 4: Parts-of-Speech Tagging – Identifying Words
Introducing parts-of-speech tagging
Creating POS-tagged corpora
Selecting a machine learning algorithm
Statistical modeling involving the n-gram approach
Developing a chunker using pos-tagged corpora
Summary
Chapter 5: Parsing – Analyzing Training Data
Introducing parsing
Treebank construction
Extracting Context Free Grammar (CFG) rules from Treebank
Creating a probabilistic Context Free Grammar from CFG
CYK chart parsing algorithm
Earley chart parsing algorithm
Summary
Chapter 6: Semantic Analysis – Meaning Matters
Introducing semantic analysis
Generation of the synset id from Wordnet
Disambiguating senses using Wordnet
Summary
Chapter 7: Sentiment Analysis – I Am Happy
Introducing sentiment analysis
Summary
Chapter 8: Information Retrieval – Accessing Information
Introducing information retrieval
Vector space scoring and query operator interaction
Developing an IR system using latent semantic indexing
Text summarization
Question-answering system
Summary
Chapter 9: Discourse Analysis – Knowing Is Believing
Introducing discourse analysis
Summary
Chapter 10: Evaluation of NLP Systems – Analyzing Performance
The need for evaluation of NLP systems
Evaluation of IR system
Metrics for error identification
Metrics based on lexical matching
Metrics based on syntactic matching
Metrics using shallow semantic matching
Summary

Book Details

ISBN 139781783989041
Paperback238 pages
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