Course Content
Algorithms for analyzing the meaning of words in documents are of crucial importance for a wide range of applications including information retrieval, automatic summarization, or automatic keyphrase extraction. The lecture puts a special focus on the semantic resources used to provide knowledge for these algorithms. Besides classical semantic wordnets like Princeton WordNet, the lecture also introduces Web 2.0 resources like Wikipedia and Wiktionary, Knowledge bases and emerging vectorial resources based on neural language models, such as word embeddings.
- Introduction to Natural Language Processing and Lexical-Semantics
- Lexical-Semantic Resources: WordNet, Wikipedia, Wiktionary
- Standardizing resources
- Lexical-semantic methods: Text Similarity,Word Sense Disambiguation
- Vector Space Models of Word Meaning
- Word and Sense Embeddings
- Applications of Embeddings
- Introduction to knowledge bases (Wikidata, Freebased, DBPedia)
- Automatic Knowledge Base Construction
- Knowledge Base Embeddings
- Dozent*in: Thomas Arnold
- Dozent*in: Jan-Christoph Klie
- Dozent*in: Christian Stab