Résumé de section

◄Part 7: Predictive Analysis (A Gentle Introduction to Machine Learning)
►Part 9: Text and Language II (Natural Language Processing)

    • Lecture content: Corpus linguistics: frequency distributions and Zipf’s law, lexical association measures, collocation, and terminology extraction; Lexico-semantic resources (Word-Net); Basics of computational linguistics: morphological normalization (inflectional and derivational morphology), part-of-speech tagging, syntactic parsing; Topic modeling.

      Tutorial content: Extracting collocations (Python libraries: nltk and spacy); Analyzing lexico-semantic knowledge in WordNet (Python library: wordnet interface from nltk); Topic modeling with latent Dirichlet allocation (Python library: gensim).

      Homework: Usage scenario – Topical exploration of the American “Lost Generation” literature (distant reading).

    • Notebook: Text Processing Fichier
    • Slides: Lexical Semantics Fichier
    • Slides: Text Representations Fichier
    • Slides: Information Extraction Fichier
    • Data: Unlabeled Reviews Fichier
◄Part 7: Predictive Analysis (A Gentle Introduction to Machine Learning)
►Part 9: Text and Language II (Natural Language Processing)