In this talk, we will explore the build-measure-learn paradigm and the role of baselines in natural language processing (NLP). We will cover the common NLP tasks of classification, clustering, search, and named entity recognition, and describe the baseline approaches that can be used for each task. We will also discuss how to move beyond these baselines through weak learning and transfer learning. By the end of this talk, attendees will have a better understanding of how to establish and improve upon baselines in NLP.
Tobias Sterbak is a Data Scientist and Software Developer from Berlin. He has been working as a freelancer in the field of Machine Learning and Natural Language Processing since 2018. On the blog www.depends-on-the-definition.com he occasionally writes about these topics. In his private life he is interested in data privacy, open source software, remote work and dogs.