NLPROC: Probabilistic Graphical Models for Natural Language Processing
- Moderator/in: Sean Papay
Semester: 2024/25 Wintersemester
NLPROC: Societal Impacts of Language Technology
- Moderator/in: Sabine Weber
Semester: 2024/25 Wintersemester
NLPROC: Explainable AI for NLP Models
- Moderator/in: Sean Papay
- Moderator/in: Sabine Weber
Semester: 2024/25 Wintersemester
NLPROC: Argument Mining in Natural Language Processing
Where does a text state an argument? Are there conditions under which it is valid? What is the quality of the argument? These questions can be answered automatically with the help of a computer, at least to some extent.
In this seminar, we will first cover the basics of natural language processing based on the student's knowledge in the class. Afterward, each week will be dedicated to specific topics in the area of argument mining, such as assessing argument quality, frames in argumentation, stance detection, emotions in arguments, convincingness in arguments, and biases. Each student picks one paper from a selected list and presents it in class.
Where does a text state an argument? Are there conditions under which it is valid? What is the quality of the argument? These questions can be answered automatically with the help of a computer, at least to some extent.
In this seminar, we will first cover the basics of natural language processing based on the student's knowledge in the class. Afterward, each week will be dedicated to specific topics in the area of argument mining, such as assessing argument quality, frames in argumentation, stance detection, emotions in arguments, convincingness in arguments, and biases. Each student picks one paper from a selected list and presents it in class.
- Moderator/in: Lynn Greschner
- Moderator/in: Roman Klinger
Semester: 2024/25 Wintersemester
NLPROC: Multimodal Text Analysis
- Moderator/in: Christopher Doyle Bagdon
- Moderator/in: Roman Klinger
Semester: 2024/25 Wintersemester
Welcome to the seminar on large language models for language natural understanding.
In this course, we will discuss what language models are, what language understanding is, and how state of the art approaches work that utilize language models to solve tasks of language understanding.
You can either register to this class through the central VC course/central registration process. You can also join outside of this process. We consider the registration for a presentation as a binding registration. The deadline will be around the second week of the term.
The class will be taught in English, except only German-speaking students participate.
In this course, we will discuss what language models are, what language understanding is, and how state of the art approaches work that utilize language models to solve tasks of language understanding.
You can either register to this class through the central VC course/central registration process. You can also join outside of this process. We consider the registration for a presentation as a binding registration. The deadline will be around the second week of the term.
The class will be taught in English, except only German-speaking students participate.
- Moderator/in: Roman Klinger
Semester: 2024/25 Wintersemester
NLPROC: Natural Language Understanding
Wir diskutieren in diesem Kurs wie man mit automatischen Methoden den Inhalt von Text verstehen kann. Wir beginnen mit Verfahren, die die Bedeutung einzelner Worte messen (Vektorrepräsentationen, Einbettungen) und erweitern dies dann zur Analyse von Phrasen, auch mit (großen) Sprachmodellen. Danach besprechen wir übliche Aufgaben im Sprachverstehen, wie Entitätserkennung, Relationserkennung, Emotions/Sentimentanalyse, Argument Analyse, aber auch Aufgaben, wie sie von großen Anweisungs-basiert trainieren Sprachmodellen gelöst werden.
Dieser Kurs wird auf Deutsch unterrichtet, falls die Teilnehmer:innen dies nicht anders wünschen.
We discuss in this class how to analyze written text and discuss methods to understand it automatically. We start with methods that measure the meaning of individual words (vector representations, embeddings) and then extend this to analyzing phrases, including with (large) language models. We then discuss common tasks in language understanding, such as entity recognition, relation recognition, emotion/sentiment analysis, argument analysis, but also tasks as solved by large instruction-based trained language models.
The course will be taught in German, unless participants wish otherwise.
Wir diskutieren in diesem Kurs wie man mit automatischen Methoden den Inhalt von Text verstehen kann. Wir beginnen mit Verfahren, die die Bedeutung einzelner Worte messen (Vektorrepräsentationen, Einbettungen) und erweitern dies dann zur Analyse von Phrasen, auch mit (großen) Sprachmodellen. Danach besprechen wir übliche Aufgaben im Sprachverstehen, wie Entitätserkennung, Relationserkennung, Emotions/Sentimentanalyse, Argument Analyse, aber auch Aufgaben, wie sie von großen Anweisungs-basiert trainieren Sprachmodellen gelöst werden.
Dieser Kurs wird auf Deutsch unterrichtet, falls die Teilnehmer:innen dies nicht anders wünschen.
We discuss in this class how to analyze written text and discuss methods to understand it automatically. We start with methods that measure the meaning of individual words (vector representations, embeddings) and then extend this to analyzing phrases, including with (large) language models. We then discuss common tasks in language understanding, such as entity recognition, relation recognition, emotion/sentiment analysis, argument analysis, but also tasks as solved by large instruction-based trained language models.
The course will be taught in German, unless participants wish otherwise.
- Moderator/in: Roman Klinger
Semester: 2024/25 Wintersemester