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.