  Real-world applications of natural language processing (NLP) are challenging.
NLP models rely heavily on supervised machine learning and require large
amounts of annotated data. These resources are often based on language data
available in large quantities, such as English newswire. However, in real-world
applications of NLP, the textual resources vary across several dimensions, such
as language, dialect, topic, and genre. It is challenging to find annotated
data of sufficient amount and quality. The objective of this thesis is to
investigate methods for dealing with such low-resource scenarios in information
extraction and natural language understanding. To this end, we study distant
supervision and sequential transfer learning in various low-resource settings.
We develop and adapt neural NLP models to explore a number of research
questions concerning NLP tasks with minimal or no training data.
