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Full Description
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.
Contents
Introduction and Overview Tommaso Caselli, Martha Palmer, Ed Hovy, and Piek Vossen; Part I. Foundational Components of Storylines: 1. The Role of Event-Based Representations and Reasoning in Language James Pustejovsky; 2. The Rich Event Ontology - Ontological Hub for Event Representations Claire Bonial, Susan W. Brown, Martha Palmer, and Ghazaleh Kazeminejad; 3. Decomposing Events and Storylines William Croft, Pavlìna Kalm and Michael Regan; 4. Extracting and Aligning Timelines Mark Finalyson, Andres Cremisini, and Mustafa Ocal; 5. Event Causality Paramita Mirza; 6. A Narratology-Based Framework for Storyline Extraction Piek Vossen, Tommaso Caselli, and Roxane Segers; Part II. Connecting the Dots: 7. The Richer Event Description Corpus for Event-Event Relations Tim O'Gorman, Kristin Wright-Bettner, and Martha Palmer; 8. Low-Resource Event Extraction via Share-and-Transfer and Remaining Challenges Heng Ji and Clare Voss; 9. Reading Certainty across Sources Ben Miller; 10. Narrative Homogeneity and Heterogeneity in Document Categories Dan Simonson and Tony Davis; 11. Exploring Machine-Learning Techniques for Linking Event Templates Jakub Piskorski, Fredi Šarić, Vanni Zavarella, and Martin Atkinson; 12. Semantic Storytelling - from Experiments and Prototypes to a Technical Solution Georg Rehm, Karolina Zaczynska, Peter Bourgonje, Malte Ostendorff, Julián Moreno-Schneider, Maria Berger, Jens Rauenbusch, André Schmidt, Mikka Wild, Joachim Böttger, Joachim Quantz, Jan Thomsen, and Rolf Fricke.
