Computational Analysis of Storylines

A review of recent computational (deep learning) approaches to understanding news and nonfiction stories.

Storylines are at the heart of information sharing. This multidisciplinary book explores automated deep learning approaches that track events that make up news and nonfiction stories. Accessible to graduate students, it highlights new research and proposes solutions to overcome the fragmentation of this lively Natural Language Processing area.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.