Introduction to Natural Language Processing (NLP). Review of linguistic preliminaries. Review of mathematical foundations. Linguistic preprocessing: tokenization, lemmatization, Part-of-Speech (PoS) tagging, stop words. Hypothesis testing. Statistical estimators in the context of NLP. Evaluation measures. Collocations, n-gram models, word-sense disambiguation. Lexical semantics. Vector space models. Word embeddings. Hidden Markov Models (HMMs) and PoS tagging. Selective applications of NLP and relation of NLP to computational social science.
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Introduction/overview for Natural Language Processing and Computational Linguistics Review of Mathematical Foundations Review of Linguistic Foundations Linguistic Preprocessing Hypothesis Testing, statistical estimators, evaluation measures Collocations, n-gram models, word-sense disambiguation Collocations, n-gram models, word-sense disambiguation Neural language models Neural language models Lexical semantics Vector space models Word embeddings Selective Applications of Natural Language Processing, Examples of Computational Social Science Selective Applications of Natural Language Processing, Examples of Computational Social Science ECTS - Workload Table: Activities Number Hours Workload Preparation for the presentation 1 3 3 Preparation for Final exam 1 30 30 Project Presentation 1 1 1 Project 1 47 47 Final exam 1 3 3 Midterm exam 1 2 2 Preparation for Midterm exam 1 20 20 Course hours 14 3 42 Project Interview 1 2 2 Total Workload: 150 Total Workload / 30: 150 / 30 5 ECTS Credits of the Course: 5 Type of Course: Lecture Teaching Methods: Lecturing - Independent study