In order that we can communicate with a computer successfully, it has to be able to interpret sentences like “I don't understand what you mean by this!”. For that it has to know what “to not understand” means and what “by this” refers to.This lecture gives an overview of different subject areas and applied methods in Natural Language Processing (NLP) and dialog modeling.Concerning NLP, the covered topics will vary in complexity such as Part-of-Speech Tagging, Sentiment Analysis, Word Sense Disambiguation (WSD) and Question Answering (QA).At the same time, various techniques will be presented with which the corresponding components can be realized. Among those are Conditional Random Fields (CRFs) and Maximum Entropy Models (MaxEnt).Furthermore, topics and methods of NLP will be emphasized which are especially relevant for realizing spoken dialog systems. In Dialog Modeling different areas like Social Dialog, Goal-Oriented Dialog, Multimodal Dialog and Error Handling will be addressed. These involve additional techniques like Partially Observable Markov Decision Processes (POMDPs).