• understand fundamentals of traditional information retrieval, including document representation, term weighting, retrieval models and system evaluation. (K2)
• understand fundamentals of rule-based information extraction, including NER, POS-tagging etc. (K2)
• understand approaches and techniques of dialogue systems (K2)
• have knowledge about related fields and current research
• can process and retrieve documents by applying by applying diverse techniques to natural language text and using current tools thereon. (K3)
• can extract information from a text corpus using advanced frameworks and libraries (K3, K6)
• are able to analyse and evaluate information extraction and information extraction tools and frameworks (K4, K5)

• Document representation: indexing, weighting (tf*idf)
• IR models: boolsch, vector space etc.
• Architectures and (natural language) user interfaces
• Evaluation of IR systems: recall, precision
• Related concepts: string similarity, thesaurus, classification, relevance feedback, query expansion, context-based IR
• IR tools and applications, eg. ElasticSearch

2) Fundamentals and concepts of information extraction (IE)

• IE types: NER, relation extraction etc.
• IE approaches and architectures: focusing on knowledge/rule-based approaches

• natural language processing/understanding (NLP/NLU)

• Evaluation of IE systems
• IE tools and applications

3)Fundamentals and concepts of dialogue systems (DS)

• Properties of human conversation, dialogue structure and state
• DS approaches, architectures, end evaluation
• Search and extraction in dialogue systems
• DS tools and applications

4) Selected topics and current research

• Information filtering & recommender systems
• Text recognition, optical character recognition (OCR)
• Multilingual/crosslingual IR
• Text summarization
• Natural language generation (NLG) etc