Computational Analysis of Storylines

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.

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 Šari¿, 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.