Inderjeet Mani – författare

This reader collects and introduces important work in linguistics, computer science, artificial intelligence, and computational linguistics on the use of linguistic devices in natural languages to situate events in time: whether they are past, present, or future; whether they are real or hypothetical; when an event might have occurred, and how long it could have lasted. In focussing on the treatment and retrieval of time-based information it seeks to lay the foundation for temporally-aware natural language computer processing systems, for example those that process documents on the worldwide web to answer questions or produce summaries. The development of such systems requires the application of technical knowledge from many different disciplines. The book is the first to bring these disciplines together, by means of classic and contemporary papers in four areas: tense, aspect, and event structure; temporal reasoning; the temporal structure of natural language discourse; and temporal annotation. Clear, self-contained editorial introductions to each area provide the necessary technical background for the non-specialist, explaining the underlying connections across disciplines.A wide range of students and professionals in academia and industry will value this book as an introduction and guide to a new and vital technology. The former include researchers, students, and teachers of natural language processing, linguistics, artificial intelligence, computational linguistics, computer science, information retrieval (including the growing speciality of question-answering), library sciences, human-computer interaction, and cognitive science. Those in industry include corporate managers and researchers, software product developers, and engineers in information-intensive companies, such as on-line database and web-service providers.

Interpreting Motion presents an integrated perspective on how language structures constrain concepts of motion and how the world shapes the way motion is linguistically expressed. Natural language allows for efficient communication of elaborate descriptions of movement without requiring a precise specification of the motion. Interpreting Motion is the first book to analyze the semantics of motion expressions in terms of the formalisms of qualitative spatialreasoning. It shows how motion descriptions in language are mapped to trajectories of moving entities based on qualitative spatio-temporal relationships. The authors provide an extensive discussion of prior research on spatial prepositions and motion verbs, devoting chapters to the compositional semantics ofmotion sentences, the formal representations needed for computers to reason qualitatively about time, space, and motion, and the methodology for annotating corpora with linguistic information in order to train computer programs to reproduce the annotation. The applications they illustrate include route navigation, the mapping of travel narratives, question-answering, image and video tagging, and graphical rendering of scenes from textual descriptions.The book is written accessibly for a broad scientific audience of linguists, cognitive scientists, computer scientists, and those working in fields such as artificial intelligence and geographic information systems.

This book investigates the nature of generalization in language and examines how language is known by adults and acquired by children. It looks at how and why constructions are learned, the relation between their forms and functions, and how cross-linguistic and language-internal generalizations about them can be explained. Constructions at Work is divided into three parts: in the first Professor Goldberg provides an overview of constructionist approaches, including the constructionist approach to argument structure, and argues for a usage-based model of grammar. In Part II she addresses issues concerning how generalizations are constrained and constructional generalizations are learned. In Part III the author shows that a combination of function and processing accounts for a wide range of language-internal and cross-linguistic generalizations. She then considers the degree to which the function of constructions explains their distribution and examines cross-linguistic tendencies in argument realization. She demonstrates that pragmatic and cognitive processes account for the data without appeal to stipulations that are language-specific.This book is an important contribution to the study of how language operates in the mind and in the world and how these operations relate. It is of central interest for scholars and graduate-level students in all branches of theoretical linguistics and psycholinguistics. It will also appeal to cognitive scientists and philosophers concerned with language and its acquisition.

This reader collects and introduces important work in linguistics, computer science, artificial intelligence, and computational linguistics on the use of linguistic devices in natural languages to situate events in time: whether they are past, present, or future; whether they are real or hypothetical; when an event might have occurred, and how long it could have lasted. Clear, self-contained editorial introductions to each area provide the necessary technical background for the non-specialist, explaining the underlying connections across disciplines.

Interpreting Motion presents an integrated perspective on how language structures constrain concepts of motion and how the world shapes the way motion is linguistically expressed. Natural language allows for efficient communication of elaborate descriptions of movement without requiring a precise specification of the motion. Interpreting Motion is the first book to analyze the semantics of motion expressions in terms of the formalisms of qualitative spatial reasoning. It shows how motion descriptions in language are mapped to trajectories of moving entities based on qualitative spatio-temporal relationships. The authors provide an extensive discussion of prior research on spatial prepositions and motion verbs, devoting chapters to the compositional semantics of motion sentences, the formal representations needed for computers to reason qualitatively about time, space, and motion, and the methodology for annotating corpora with linguistic information in order to train computer programs to reproduce the annotation. The applications they illustrate include route navigation, the mapping of travel narratives, question-answering, image and video tagging, and graphical rendering of scenes from textual descriptions.The book is written accessibly for a broad scientific audience of linguists, cognitive scientists, computer scientists, and those working in fields such as artificial intelligence and geographic information systems.

Time is a key aspect of narrative. It can advance a story, illuminate its role in our daily lives, and help us understand how events unfold. In this groundbreaking interdisciplinary work, Inderjeet Mani uses recent developments in linguistics and computer science to analyze the use of time in narrative form. The Imagined Moment outlines directions for an emerging discipline of "corpus narratology," an approach involving the computer analysis and interpretation of multimillion-word collections of narrative text. This approach, Mani explains, could alter the very foundations of narrative theory. Accordingly, he develops a computer representation for timelines and applies it to a variety of literary works. Among these are such classics as One Hundred Years of Solitude, "A Hunger Artist," Swann's Way, Jealousy, Candide, and "The Short Happy Life of Francis Macomber." Along the way, Mani considers stories embedded in temporal cycles; the cognitive processes involved in the construal of events in time; the modeling of narrative progression in terms of changes in readers' evaluation of characters; the study of variations of tempo in fiction; and time in computer-mediated forms of storytelling.

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 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

This second edition provides an overview of the principal problems, approaches, and challenges in modeling the narrative structure of stories. The book builds upon the prior edition by incorporating an additional focus on generative artificial intelligence and its applications for narratology. The author explains the computational representations of narrative elements, such as characters, events, causality, and time, proposing new methods for representing embedded narratives, fictional entities, tempo, and audience response. The book discusses the rise of generative AI systems, including large language models and transformers. The author explores the current applications of these systems and the extent to which they can be used to automate the cognitive aspects of narrative understanding and generation.