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I. Part 1: Basics
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I. Part 1: Basics
by Alex Thomas
Natural Language Processing with Spark NLP
Preface
Why Natural Language Processing can be difficult
Background
I. Part 1: Basics
1. Getting Started
Introduction
Setting up your environment
Prerequisites
Starting Apache Spark
Checking out the code
Getting Familiar with Apache Spark
Starting Apache Spark with Spark NLP
Loading & viewing data in Apache Spark
Hello World with Spark NLP
2. Natural Language Basics
What is natural language?
Origins of language
Spoken language vs. written language
Linguistics
Phonetics & Phonology
Morphology
Syntax
Semantics
Sociolinguistics: Dialects, Registers, and Other Varieties
Formality
Context
Pragmatics
Roman Jakobson
How to use Pragmatics
Writing Systems
Origins
Alphabets
Abjads
Abugidas
Syllabaries
Logographs
Encodings
ASCII
Unicode
UTF-8
Exercise: Tokenizing
Resources
3. NLP on Apache Spark
Parallelism, concurrency, distributing computation
Parallelization before Apache Hadoop
MapReduce and Apache Hadoop
Apache Spark
Architecture of Apache Spark
Physical architecture
Logical architecture
Section 3.3 - SparkSQL and Spark MLLib
Transformers
Estimators and Models
Evaluators
NLP libraries
Functionality Libraries
Annotation Libraries
NLP in other libraries
Spark NLP
An annotation library
Stages
Pretrained pipelines
Finisher
Exercises
4. Deep Learning Basics
Gradient Descent
Backpropagation
Convolutional Neural Networks
Recurrent Neural Networks
Exercises
Resources
II. Building Blocks
5. Processing Words
Tokenization
Vocabulary reduction
Bag-of-Words
n-Grams
Visualizing: Word and Document Distributions
Exercises
Resources
6. Information Retrieval
Inverted Indices
Vector Space Model
Stop word removal
Inverse Document Frequency
In Spark
Exercises
Resources
7. Classification and Regression
Bag-of-Words Features
Regular Expression Features
Feature Selection
Modeling
Iteration
Exercises
8. Sequence Modeling with Keras
Sentence segmentation
Section segmentation
Part-of-speech tagging
Chunking and Syntactic Parsing
Language models
Recurrent Neural Networks
Exercises
Resources
9. Information Extraction
Named Entity Recognition
Coreference Resolution
Assertion Status Detection
Relationship Extraction
Summary
Exercises
10. Topic Modeling
K-Means
Exercises
11. Embeddings
word2vec
GloVe
fastText
Transformer
ELMo, BERT, and XLNet
doc2vec
Exercise
III. Applications
12. Sentiment Analysis & Emotion Detection
Problem statement & Constraints
Plan the project
Design the solution
Implement the solution
Test & Measure the solution
Review
13. Building Knowledge Bases
Problem statement & Constraints
Plan the project
Design the solution
Implement the solution
Test & Measure the solution
Business metrics
Model-centric metrics
Infrastructure metrics
Process metrics
14. Semantic Search
Problem statement & Constraints
Plan the project
Design the solution
Implement the solution
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Preface
Next
Next Chapter
1. Getting Started
Part I.
Part 1: Basics
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