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Index
Python Natural Language Processing Title Page Copyright Python Natural Language Processing Credits Foreword About the Author Acknowledgement About the Reviewers www.PacktPub.com Why subscribe? Customer Feedback Table of Contents Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions Introduction Understanding natural language processing Understanding basic applications Understanding advanced applications Advantages of togetherness - NLP and Python Environment setup for NLTK Tips for readers Summary Practical Understanding of a Corpus and Dataset What is a corpus? Why do we need a corpus? Understanding corpus analysis Exercise Understanding types of data attributes Categorical or qualitative data attributes Numeric or quantitative data attributes Exploring different file formats for corpora Resources for accessing free corpora Preparing a dataset for NLP applications Selecting data Preprocessing the dataset Formatting Cleaning Sampling Transforming data Web scraping Summary Understanding the Structure of a Sentences Understanding components of NLP Natural language understanding Natural language generation Differences between NLU and NLG Branches of NLP Defining context-free grammar Exercise Morphological analysis What is morphology? What are morphemes? What is a stem? What is morphological analysis? What is a word? Classification of morphemes Free morphemes Bound morphemes Derivational morphemes Inflectional morphemes What is the difference between a stem and a root? Exercise Lexical analysis What is a token? What are part of speech tags? Process of deriving tokens Difference between stemming and lemmatization Applications Syntactic analysis What is syntactic analysis? Semantic analysis What is semantic analysis? Lexical semantics Hyponymy and hyponyms Homonymy Polysemy What is the difference between polysemy and homonymy? Application of semantic analysis Handling ambiguity Lexical ambiguity Syntactic ambiguity Approach to handle syntactic ambiguity Semantic ambiguity Pragmatic ambiguity Discourse integration Applications Pragmatic analysis Summary Preprocessing Handling corpus-raw text Getting raw text Lowercase conversion Sentence tokenization Challenges of sentence tokenization Stemming for raw text Challenges of stemming for raw text Lemmatization of raw text Challenges of lemmatization of raw text Stop word removal Exercise Handling corpus-raw sentences Word tokenization Challenges for word tokenization Word lemmatization Challenges for word lemmatization Basic preprocessing Regular expressions Basic level regular expression Basic flags Advanced level regular expression Positive lookahead Positive lookbehind Negative lookahead Negative lookbehind Practical and customized preprocessing Decide by yourself Is preprocessing required? What kind of preprocessing is required? Understanding case studies of preprocessing Grammar correction system Sentiment analysis Machine translation Spelling correction Approach Summary Feature Engineering and NLP Algorithms Understanding feature engineering What is feature engineering? What is the purpose of feature engineering? Challenges Basic feature of NLP Parsers and parsing Understanding the basics of parsers Understanding the concept of parsing Developing a parser from scratch Types of grammar Context-free grammar Probabilistic context-free grammar Calculating the probability of a tree Calculating the probability of a string Grammar transformation Developing a parser with the Cocke-Kasami-Younger Algorithm Developing parsers step-by-step Existing parser tools The Stanford parser The spaCy parser Extracting and understanding the features Customizing parser tools Challenges POS tagging and POS taggers Understanding the concept of POS tagging and POS taggers Developing POS taggers step-by-step Plug and play with existing POS taggers A Stanford POS tagger example Using polyglot to generate POS tagging Exercise Using POS tags as features Challenges Name entity recognition Classes of NER Plug and play with existing NER tools A Stanford NER example A Spacy NER example Extracting and understanding the features Challenges n-grams Understanding n-gram using a practice example Application Bag of words Understanding BOW Understanding BOW using a practical example Comparing n-grams and BOW Applications Semantic tools and resources Basic statistical features for NLP Basic mathematics Basic concepts of linear algebra for NLP Basic concepts of the probabilistic theory for NLP Probability Independent event and dependent event Conditional probability TF-IDF Understanding TF-IDF Understanding TF-IDF with a practical example Using textblob Using scikit-learn Application Vectorization Encoders and decoders One-hot encoding Understanding a practical example for one-hot encoding Application Normalization The linguistics aspect of normalization The statistical aspect of normalization Probabilistic models Understanding probabilistic language modeling Application of LM Indexing Application Ranking Advantages of features engineering Challenges of features engineering Summary Advanced Feature Engineering and NLP Algorithms Recall word embedding Understanding the basics of word2vec Distributional semantics Defining word2vec Necessity of unsupervised distribution semantic model - word2vec Challenges Converting the word2vec model from black box to white box Distributional similarity based representation Understanding the components of the word2vec model Input of the word2vec Output of word2vec Construction components of the word2vec model Architectural component Understanding the logic of the word2vec model Vocabulary builder Context builder Neural network with two layers Structural details of a word2vec neural network Word2vec neural network layer's details Softmax function Main processing algorithms Continuous bag of words Skip-gram Understanding algorithmic techniques and the mathematics behind the word2vec model Understanding the basic mathematics for the word2vec algorithm Techniques used at the vocabulary building stage Lossy counting Using it at the stage of vocabulary building Applications Techniques used at the context building stage Dynamic window scaling Understanding dynamic context window techniques Subsampling Pruning Algorithms used by neural networks Structure of the neurons Basic neuron structure Training a simple neuron Define error function Understanding gradient descent in word2vec Single neuron application Multi-layer neural networks Backpropagation Mathematics behind the word2vec model Techniques used to generate final vectors and probability prediction stage Hierarchical softmax Negative sampling Some of the facts related to word2vec Applications of word2vec Implementation of simple examples Famous example (king - man + woman) Advantages of word2vec Challenges of word2vec How is word2vec used in real-life applications? When should you use word2vec? Developing something interesting Exercise Extension of the word2vec concept Para2Vec Doc2Vec Applications of Doc2vec GloVe Exercise Importance of vectorization in deep learning Summary Rule-Based System for NLP Understanding of the rule-based system What does the RB system mean? Purpose of having the rule-based system Why do we need the rule-based system? Which kind of applications can use the RB approach over the other approaches? Exercise What kind of resources do you need if you want to develop a rule-based system? Architecture of the RB system General architecture of the rule-based system as an expert system Practical architecture of the rule-based system for NLP applications Custom architecture - the RB system for NLP applications Exercise Apache UIMA - the RB system for NLP applications Understanding the RB system development life cycle Applications NLP applications using the rule-based system Generalized AI applications using the rule-based system Developing NLP applications using the RB system Thinking process for making rules Start with simple rules Scraping the text data Defining the rule for our goal Coding our rule and generating a prototype and result Exercise Python for pattern-matching rules for a proofreading application Exercise Grammar correction Template-based chatbot application Flow of code Advantages of template-based chatbot Disadvantages of template-based chatbot Exercise 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 Machine Learning for NLP Problems Understanding the basics of machine learning Types of ML Supervised learning Unsupervised learning Reinforcement learning Development steps for NLP applications Development step for the first iteration Development steps for the second to nth iteration Understanding ML algorithms and other concepts Supervised ML Regression Classification ML algorithms Exercise Unsupervised ML k-means clustering Document clustering Advantages of k-means clustering Disadvantages of k-means clustering Exercise Semi-supervised ML Other important concepts Bias-variance trade-off Underfitting Overfitting Evaluation matrix Exercise Feature selection Curse of dimensionality Feature selection techniques Dimensionality reduction Hybrid approaches for NLP applications Post-processing Summary Deep Learning for NLU and NLG Problems An overview of artificial intelligence The basics of AI Components of AI Automation Intelligence Stages of AI Machine learning Machine intelligence Machine consciousness Types of artificial intelligence Artificial narrow intelligence Artificial general intelligence Artificial superintelligence Goals and applications of AI AI-enabled applications Comparing NLU and NLG Natural language understanding Natural language generation A brief overview of deep learning Basics of neural networks The first computation model of the neuron Perceptron Understanding mathematical concepts for ANN Gradient descent Calculating error or loss Calculating gradient descent Activation functions Sigmoid TanH ReLu and its variants Loss functions Implementation of ANN Single-layer NN with backpropagation Backpropagation Exercise Deep learning and deep neural networks Revisiting DL The basic architecture of DNN Deep learning in NLP Difference between classical NLP and deep learning NLP techniques Deep learning techniques and NLU Machine translation Deep learning techniques and NLG Exercise Recipe summarizer and title generation Gradient descent-based optimization Artificial intelligence versus human intelligence Summary 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 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 Installation Guide Installing Python, pip, and NLTK Installing the PyCharm IDE Installing dependencies Framework installation guides Drop your queries Summary
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