Learn how to write programs that analyze written language. The course will balance theoretical foundations with practical examples using the Python programming language. No prior experience with libraries such as NLTK or scikit-learn is required for this course.
1 - Foundations:
This first section provides the basic tools and techniques to get started with Natural Language Processing.
Overview on NLP applications and the Python:
Working with text:
• Text pre-processing
• Regular Expressions
Word frequencies and co-occurrences:
• Stop-words and Zipf's Law
• Mining topics of interest with co-occurrences
• Word embeddings and document embeddings
2 - Topic Modelling:
This section aims at improving our understanding of a document, or a collection of documents, using techniques that go beyond simple word frequencies.
• Bird's-eye view on a document or a dataset
• Navigating topics and sub-topics in a document or a dataset
3 - Text Classification:
This section tackles the problem of classifying documents into a set of predefined categories.
• Categorising documents
• Topic Classification
• Sentiment Analysis
• Model evaluation: assessing classification quality
• Model introspection: explaining the classification results
4 - Overview on Advanced Applications:
The last section offers an outlook on advanced NLP problems, so delegates are equipped with ideas and techniques to tackle more specific applications.
Named Entity Recognition:
• Identifying named entity in text
• Extracting the most useful sentences from a document or a collection of documents
Natural Language Generation:
• Creating an AI bot that talks like Shakespeare (or Trump)
• English course material, English speaking instructor