Abschnittsübersicht

◄Part 8: Text and Language I (Computational Linguistics)
►Part 10: Guest lecture

    • Lecture content: Language modeling and distributional semantics; Sparse and dense text representations for natural language processing; Information extraction – recognizing mentions of entities and events in text; 

      Tutorial content: Sparse text representations (Python library: scikit-learn) and dense text representations (Python library: gensim); Extracting named entities from text in different languages (Python library: spacy); Text classification with traditional (logistic regression) (Python libraries: scikit-learn).

      Homework : Usage scenario – Detecting hate speech in social media posts. Interpreting a (simple linear) model: Which terms are strong indicators of a class?

◄Part 8: Text and Language I (Computational Linguistics)
►Part 10: Guest lecture