  Artificial Intelligence (AI) has huge impact on our daily lives with
applications such as voice assistants, facial recognition, chatbots,
autonomously driving cars, etc. Natural Language Processing (NLP) is a
cross-discipline of AI and Linguistics, dedicated to study the understanding of
the text. This is a very challenging area due to unstructured nature of the
language, with many ambiguous and corner cases. In this thesis we address a
very specific area of NLP that involves the understanding of entities (e.g.,
names of people, organizations, locations) in text. First, we introduce a
radically different, entity-centric view of the information in text. We argue
that instead of using individual mentions in text to understand their meaning,
we should build applications that would work in terms of entity concepts. Next,
we present a more detailed model on how the entity-centric approach can be used
for the entity linking task. In our work, we show that this task can be
improved by considering performing entity linking at the coreference cluster
level rather than each of the mentions individually. In our next work, we
further study how information from Knowledge Base entities can be integrated
into text. Finally, we analyze the evolution of the entities from the evolving
temporal perspective.
