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

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.

This book constitutes the refereed proceedings of the 14th International Conference on Computational Collective Intelligence, ICCCI 2022, held in Hammamet, Tunisia, in September 2022. The 56 full papers and 10 short papers were carefully reviewed and selected from 420 submissions. The papers are grouped in topical ​sections on collective intelligence and collective decision-making; deep learning techniques; natural language processing; data minning and machine learning; knowledge engineering and semantic web; computer vision techniques; social networks and intelligent systems; cybersecurity and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; applications for industry 4.0.

This collection examines the promise and limitations for computer-assisted language learning of emerging speech technologies: speech recognition, text-to-speech synthesis, and acoustic visualization. Using pioneering research from contributors based in the US and Europe, this volume illustrates the uses of each technology for learning languages, the problems entailed in their use, and the solutions evolving in both technology and instructional design. To illuminate where these technologies stand on the path from research toward practice, the book chapters are organized to reflect five stages in the maturation of learning technologies: basic research, analysis of learners’ needs, adaptation of technologies to meet needs, development of prototypes to incorporate adapted technologies, and evaluation of prototypes. The volume demonstrates the progress in employing each class of speech technology while pointing up the effort that remains for effective, reliable application to language learning.

The emergence and widespread use of personal computers and network technologies has seen the development of interest in the use of computers to support cooperative work. This volume presents the proceedings of the seventh European conference on Computer Supported Cooperative Work (CSCW). This is a multidisciplinary area which embraces both the development of new technologies and an understanding of the grounding of CSCW technology in organizational practices. These proceedings contain a collection of papers that encompass activities in the field, including distributed virtual environments, new models and architectures for groupware systems, studies of communication and coordination among mobile actors, studies of cooperative work in heterogeneous settings, studies of groupware systems in use in real-world settings, and theories and techniques to support the development of cooperative applications. The papers present emerging technologies alongside new methods and approaches to the development of this important class of applications. The work in this volume represents the best of the current research and practice within CSCW. The collection of papers presented here will appeal to both researchers and practitioners alike as they combine an understanding of the nature of work with the possibility offered by new technologies.

Sentiment analysis has gained widespread adoption in many fields, but not—until now—in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model—or set of models—depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

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. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions

The consortium’s objective is to establish partnerships that facilitate sharing of IP assets to advance the discovery and development of new drugs, vaccines, and diagnostics for NTDs, malaria, and tuberculosis.

Conducting computer analyses for the purposes of revealing information of significance to the press represents an extension of one of the most important forms of American journalism into the contemporary era of new technologies. Investigative reporting had its start with the establishment of the metropolitan newspaper during the early decades of the 1900s. At the time, it was a continuation of the evolving tradition of freedom of the press that had characterized American political life since colonial times. As it developed, investigative reporting stressed facts rather than the opinions of the editor or reporter. In turn, that tradition had its own intellectual roots. Today, computer-assisted investigative reporting (CAIR) extends that "marketplace of ideas" into systematic examinations of the electronic records of government. In addition, computer analyses of other kinds of information systematically gathered by journalists can provide the press with insights into trends and patterns unlikely to be revealed by other means. This unique volume addresses procedures and issues in investigative journalism that have not been explained in other publications. It sets forth -- for the first time -- a detailed and specific methodology for conducting computer-assisted investigative analyses of both large and small scale electronic records of government and other agencies. That methodology consists of the logic of inquiry, strategies for reaching valid conclusions, and rules for reporting what has been revealed by the analyses to the public in clear ways. Such systematic methodologies are essential in social and other sciences and the development of a counterpart for investigative journalism has been badly needed. That systematic methodology is developed within a context that explains the origin and major characteristics of those elements that have come together in American society to make computer-assisted investigative reporting both possible and increasingly a part of standard newsroom practices. These include the development of traditional investigative journalism, the evolution of computer technology, the use of computers by government to keep records, the legal evolution of freedom of information laws, the rapid adoption of computers in newsrooms, the increasing importance of precision journalism, and the sharp increase in recent times of computer-assisted investigative reporting by American newspapers both large and small. The issues addressed in this book are discussed in a very readable context with an abundance of examples and illustrations drawn from the real world of journalism as it is practiced daily in newsrooms around the country. Explanations of concepts, principles, and procedures are set forth in layperson's terms that require very little in the way of knowledge of computers or statistical methods.

This unique book presents real world success stories of collaboration between mathematicians and industrial partners, showcasing first-hand case studies, and lessons learned from the experiences, technologies, and business challenges that led to the successful development of industrial solutions based on mathematics. It shows the crucial contribution of mathematics to innovation and to the industrial creation of value, and the key position of mathematics in the handling of complex systems, amplifying innovation. Each story describes the challenge that led to the industrial cooperation, how the challenge was approached and how the solutions were achieved and implemented. When brought together, they illustrate the versatile European landscape of projects in almost all areas of applied mathematics and across all business sectors. This book of success stories has its origin in the Forward Look about Mathematics and Industry that was funded by the European Science Foundation (ESF) and coordinated by the Applied Mathematics Committee of the European Mathematical Society (EMS). In each of these success stories, researchers, students, entrepreneurs, policy makers and business leaders in a range of disciplines will find valuable material and important lessons that can be applied in their own fields.​