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Event structures are central in Linguistics and Artificial
Intelligence research: people can easily refer to changes in the
world, identify their participants, distinguish relevant
information, and have expectations of what can happen next. Part of
this process is based on mechanisms similar to narratives, which
are at the heart of information sharing. But it remains difficult
to automatically detect events or automatically construct stories
from such event representations. This book explores how to handle
today's massive news streams and provides multidimensional,
multimodal, and distributed approaches, like automated deep
learning, to capture events and narrative structures involved in a
'story'. This overview of the current state-of-the-art on event
extraction, temporal and casual relations, and storyline extraction
aims to establish a new multidisciplinary research community with a
common terminology and research agenda. Graduate students and
researchers in natural language processing, computational
linguistics, and media studies will benefit from this book.
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