Studying narratives is an ideal method to gain a good understanding of how various aspects of human information are organized and integrated. The concept and methods of a narrative, which have been explored in narratology and literary theories, are likely to be connected with contemporary information studies in the future, including those in computational fields such as AI, and in cognitive science. This will result in the emergence of a significant conceptual and methodological foundation for various technologies of novel contents, media, human interface, etc. Post-Narratology Through Computational and Cognitive Approaches explores the new possibilities and directions of narrative-related technologies and theories and their implications on the innovative design, development, and creation of future media and contents (such as automatic narrative or story generation systems) through interdisciplinary approaches to narratology that are dependent on computational and cognitive studies. While highlighting topics including artificial intelligence, narrative analysis, and rhetoric generation, this book is ideally designed for designers, creators, developers, researchers, and advanced-level students.

The use of cognitive science in creating stories, languages, visuals, and characters is known as narrative generation, and it has become a trending area of study. Applying artificial intelligence (AI) techniques to story development has caught the attention of professionals and researchers; however, few studies have inherited techniques used in previous literary methods and related research in social sciences. Implementing previous narratology theories to current narrative generation systems is a research area that remains unexplored. Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation is a collection of innovative research on the analysis of current practices in narrative generation systems by combining previous theories in narratology and literature with current methods of AI. The book bridges the gap between AI, cognitive science, and narratology with narrative generation in a broad sense, including other content generation, such as a novels, poems, movies, computer games, and advertisements. The book emphasizes that an important method for bridging the gap is based on designing and implementing computer programs using knowledge and methods of narratology and literary theories. In order to present an organic, systematic, and integrated combination of both the fields to develop a new research area, namely post-narratology, this book has an important place in the creation of a new research area and has an impact on both narrative generation studies, including AI and cognitive science, and narrative studies, including narratology and literary theories. It is ideally designed for academicians, researchers, and students, as well as enterprise practitioners, engineers, and creators of diverse content generation fields such as advertising production, computer game creation, comic and manga writing, and movie production.

Narrative generation can be applied to systematic frameworks that cover theoretical and philosophical thoughts of narratives and narrative generation, analytical research of related narrative genres and narrative works, and narrative works writing and creation using narrative generation systems. The design and development of narrative generation systems refers to the themes regarding narrative work creation as arts and literature through narrative generation systems beyond narrative generation systems as a technology. Internal and External Narrative Generation Based on Post-Narratology: Emerging Research and Opportunities is an essential scholarly publication that explores the creation of narrative systems using practical frameworks and advanced narrative analysis. Highlighting a range of topics such as marketing, synthetic narrative, and application systems, this book is ideal for academicians, information technology professionals, designers, developers, researchers, and students.

The concept of narrative has exerted a strong influence on a wide range of fields, from the humanities such as literature (and art and entertainment) to social studies, psychiatry, and psychology. The framework that allows access to narratives across a wide range of areas, from science to the humanities, has the potential to be improved as a fusion of cognitive science and artificial intelligence. Toward an Integrated Approach to Narrative Generation: Emerging Research and Opportunities is a critical scholarly book that focuses on the significance of narratives and narrative generation in various aspects of human society. Featuring an array of topics such as philosophy, narratology, and advertising, this book is ideal for software developers, academicians, philosophy professionals, researchers, and students in the fields of cognitive studies, literary studies, and digital content design and development.

Studying narratives is often the best way to gain a good understanding of how various aspects of human information are organized and integrated—the narrator employs specific informational methods to build the whole structure of a narrative through combining temporally constructed events in light of an array of relationships to the narratee and these methods reveal the interaction of the rational and the sensitive aspects of human information. Computational and Cognitive Approaches to Narratology discusses issues of narrative-related information and communication technologies, cognitive mechanism and analyses, and theoretical perspectives on narratives and the story generation process. Focusing on emerging research as well as applications in a variety of fields including marketing, philosophy, psychology, art, and literature, this timely publication is an essential reference source for researchers, professionals, and graduate students in various information technology, cognitive studies, design, and creative fields.

This book constitutes the refereed proceedings of the 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, which was planned to be held in Kyoto, Japan, in November/December 2020, but it was held virtually due to the COVID-19 pandemic. The 10 full, 15 short, 4 practitioners, and 10 work-in-progress papers presented in this volume were carefully reviewed and selected from 79 submissions. The papers were organized in topical sections named: natural language processing; knowledge structures; citation data analysis; user analytics; application of cultural and historical data; social media; metadata and infrastructure; and scholarly data mining.

The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists.