Want to learn how to build an Internet search engine from scratch? Want to learn the fundaments for natural language processing and textual document processing?
In this class, offered as a lecture in a lecture hall with all lectures being recorded and made available as videos, we discuss fundamental data structures for information retrieval, ranking, classification, or clustering of documents. This class also creates the fundament for further natural language processing methods, including natural language understanding and deep learning for natural language processing.
Participation in the lectures is not mandatory, but if you like to interact, ask questions and actively discussed, very appreciated. Active participation in the exercises is expected, but participation is also not mandatory.
In more detail, the following topics will be part of this class:
• Boolean Retrieval, Term Vocabularies and Postings Lists, Dictionaries and Tolerant Retrieval, Spelling Correction, Index Construction, Compression, Scoring, Ranking, Evaluation, Query Expansion, Probabilistic IR
• Text Classification, Naïve Bayes, MaxEnt Classifier, kNN, Neural Networks, Feature Selection, Vector space classification, Document similarities
• Learning to Rank, Learning to Score
• Flat clustering, Hierarchical Clustering, Evaluation
In this class, offered as a lecture in a lecture hall with all lectures being recorded and made available as videos, we discuss fundamental data structures for information retrieval, ranking, classification, or clustering of documents. This class also creates the fundament for further natural language processing methods, including natural language understanding and deep learning for natural language processing.
Participation in the lectures is not mandatory, but if you like to interact, ask questions and actively discussed, very appreciated. Active participation in the exercises is expected, but participation is also not mandatory.
In more detail, the following topics will be part of this class:
• Boolean Retrieval, Term Vocabularies and Postings Lists, Dictionaries and Tolerant Retrieval, Spelling Correction, Index Construction, Compression, Scoring, Ranking, Evaluation, Query Expansion, Probabilistic IR
• Text Classification, Naïve Bayes, MaxEnt Classifier, kNN, Neural Networks, Feature Selection, Vector space classification, Document similarities
• Learning to Rank, Learning to Score
• Flat clustering, Hierarchical Clustering, Evaluation
- Moderator/in: Roman Klinger
Semester: 2024 Sommersemester
Emotions are a concept that we all know and that feels familiar. Nevertheless it’s quite hard to define it, and making computer to understand what an emotion is requires to formalize the concept. In this class, we will learn about emotion theories from psychology and work in natural language processing/computer science that formalizes them into computational models. We will learn how to build automatic emotion detection models and how to apply them, for instance for social media analysis, computational literary studies, or computational social sciences. We focus on text as the only modality in the class.
There is no dedicated book on emotion analysis, but some material can be helpful:
• The book on affective computing by Rosalind W. Picard. This book is broader than this class and discusses emotions and affect as it is modelled with computers in more general.
• The book on sentiment analysis by Bing Liu. This book focuses on text analysis, but more on opinion mining and sentiment analysis, less on emotions.
• The handbook of emotions by Feldman Barrett, Lewis, Haviland-Jones. This book provides a very good overview on the theories of emotions from a psychological perspective.
There is no dedicated book on emotion analysis, but some material can be helpful:
• The book on affective computing by Rosalind W. Picard. This book is broader than this class and discusses emotions and affect as it is modelled with computers in more general.
• The book on sentiment analysis by Bing Liu. This book focuses on text analysis, but more on opinion mining and sentiment analysis, less on emotions.
• The handbook of emotions by Feldman Barrett, Lewis, Haviland-Jones. This book provides a very good overview on the theories of emotions from a psychological perspective.
- Moderator/in: Roman Klinger
Semester: 2024 Sommersemester