This course covers the fundamentals and recent understandings of coreference resolution strategies. Coreference resolution is the task of identifying all expressions pointing to the same entity in a text. It is an important part of natural language understanding, and therefore, a crucial step in many higher level NLP tasks such as text summarization, question answering, and information extraction. In this course, we will cover the linguistic concepts relevant to coreference resolution and a variety of architectures for resolution, from simple deterministic baseline algorithms to state-of-the-art neural models.  We will also go over the automatic coreference resolution systems and discuss the methods for evaluating the outcome of these automated systems.

Navigating the whole process of a publishing a paper can be challenging when we are starting our careers. Reading and writing scientific literature demands practice, data collection and annotation is not a simple task as it may sound and ML experiments can easily turn into chaos without proper organisation. Besides, there are good (and bad) scientific practices that every researcher should be aware of in order to responsibly contribute to the community. In this course, we’ll cover the main steps of the current scientific process of NLP and Computational Linguistics and, more generally, Machine Learning.

Programmierung II, Bachelor Computerlinguistik



🐍 Wir verwenden Python3.

👩‍💻 Es wird einen Mix aus individuellen Aufgaben und (Remote-)Gruppenarbeit geben.


Alle weiteren Informationen sowie den Zugang erhalten Sie nach der Einschreibung in PULS.