Building Pipelines for Natural Language Understanding with Spark

Table of contents

1. Welcome to the Course
2. Part 1: Building a natural language processing and entity extraction pipeline on Scala Spark
   1. Notebook 1: Introduction
   2. Annotation Library
   3. Basic Annotators
   4. Vocabulary Analysis
   5. Exercise: Building a stopword annotator
3. Part 2: Machine Learning Applications for Statistical Natural Language Understanding at Scale
   1. Notebook 2: Introduction
   2. Model-based Annotators
   3. Creating a Binary Classifier
   4. Exercise: Predicting score or popularity
4. Part 3: Topic Modeling on Natural Language with Scala, Spark and MLLib
   1. Notebook 3: Introduction
   2. K-Means clustering
   3. LDA topic modeling
   4. Exercise: Using topics for score or popularity prediction
5. Part 4: Deep Learning Applications for Natural Language Understanding with Scala, Spark and MLLib
   1. Notebook 4: Introduction
   2. Word2Vec
   3. Expanding genre entity lists
   4. Exercise: Using Word2Vec based features for score or popularity prediction