Lecture 1 provides an overview on NLP, it's main goals, challenges and applications. Moreover in this lecture the overall structure of the course and it's goals are described

Lecture 2 is concerned with text preprocessing and normalisation. E.g. how to access data from the web, clean HTML, segment text in sentences and words (tokenization) and transform words to their baseforms (stemming and lemmatization). For this the most important encoding types are discussed. Moreover, the application of regular expressions in NLP is introduced and some popular stemmers are applied.

Lecture 3 introduces algorithms for text classification and methods to measure and evaluate performance of these algorithms

In Lecture 4 is concerned with the use of corpora and lexical resources in NLP.

Lecture 5is concerned with morphological- and lexical analysis, Part of Speech tagging and taggsets

Lecture 6 introduces Markov Chains and Hidden Markov Models and their application in NLP, e.g. for POS-Tagging.

Lecture 7 intoduces chunking (shallow parsing) and how this technique can be applied for Entity-Relaction Extraction. The application of chunking provides answers to questions like which objects are mentioned in a text ? and which relations between these objects are described in the text ?

Lectures 8 parsing techniques for syntactical analysis are presented. Context Free Grammers, their pros and cons and finally feature based grammars are introduced.

Lecture 9 introduces techniques for the extraction of meaning (semantics) from text (semantic analysis). Central in this lecture is the introduction of Lambda-Typen Logic and how the constituentes of natural language sentences can be transformed to this type of logic.

1. S. Bird, E. Klein, E. Loper; Natural Language Processing with Python; O'Reilly, 2009
2. C.D. Manning, H. Schütze; Foundations of Statistical Language Processing; MIT Press, 1999
3. N. Indurkhya, F.J. Damerau (Editors); Handbook of Natural Language Processing; Chapman & Hall/ CRC, 2010