Text Mining Training - Australia

Text Mining Training​ Course Outline

Introduction to Text Mining

• What is Text Mining?
• Text Mining Systems Architecture

Core Text Mining Operations

• Text Mining Operations
• Text Mining Query Languages

Text Mining Preprocessing Techniques

• Task-Oriented Approaches

Categorisation

• Text Categorisation Applications
• Document Representation
• Knowledge Engineering and Machine Learning Approach to TC
• Using Unlabeled Data
• Evaluating Text Classifiers

Introduction to Clustering

• Clustering Tasks in Text Analysis
• Clustering Algorithms
• Clustering of Textual Data

Information Extraction (IE)

• Define Information Extraction
• IE Systems Architecture
• Anaphora Resolution
• IE Inductive Algorithms
• Structural Information Extraction (IE)

Probabilistic Models for IE

• Hidden Markov Models
• Stochastic Context-Free Grammar
• Maximal Entropy Modeling
• Maximal Entropy Markov Models
• Conditional Random Fields

Preprocessing Applications

• HMM to Textual Analysis Applications
• Using MEMM for IE
• Applications of CRFs to Textual Analysis
• Using SCFG Rules
• Bootstrapping

Presentation-Layer Considerations

• Browsing
• Accessing Constraints and Simple Specification Filters at the Presentation Layer
• Accessing the Underlying Query Language

Visualisation Approaches

• Architectural Considerations
• Text Mining Visualisation Approaches
• Visualisation Techniques in Link Analysis

Introduction to Link Analysis

• Automatic Layout of Networks
• Paths and Cycles in Graphs
• Centrality
• Partitioning of Networks
• Networks Pattern Matching