A Flexible Framework for the Creation of Narrative-Centered Tools

To better support the creation of narrative-centered tools, developers need a flexible framework to integrate, catalog, select, and reuse narrative models. Computational models of narrative enable the creation of software tools to aid narrative processing, analysis, and generation. Narrative-centered tools explicitly or implicitly embody one or more models of narrative by their definition. However, narrative model creation is often expensive and difficult with no guaranteed benefit to the end system. This paper describes our preliminary approach towards creating the SONNET narrative framework, a flexible framework to integrate, catalog, select, and reuse narrative models, thereby lowering development costs and improving benefits from each model. The framework includes a lightweight ontology language for the definition of key terms and interrelationships among them. The framework specifies model metadata to allow developers to discover and understand models more readily. We discuss the structure of this framework and ongoing development incorporating narrative models

The Need for Multi-Aspectual Representation of Narratives in Modelling their Creative Process

Existing approaches to narrative construction tend to apply basic engineering principles of system design which rely on identifying the most relevant feature of the domain for the problem at hand, and postulating an initial representation of the problem space organised around such a principal feature. Some features that have been favoured in the past include: causality, linear discourse, underlying structure, and character behavior. The present paper defends the need for simultaneous consideration of as many as possible of these aspects when attempting to model the process of creating narratives, together with some mechanism for distributing the weight of the decision processes across them. Humans faced with narrative construction may shift from views based on characters to views based on structure, then consider causality, and later also take into account the shape of discourse. This behavior can be related to the process of representational re-description of constraints as described in existing literature on cognitive models of the writing task. The paper discusses how existing computational models of narrative construction address this phenomenon, and argues for a computational model of narrative explicitly based on multiple aspects

Learning Components of Computational Models from Texts

The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine

A domain-independent model of suspense in narrative

Many computational models of narrative have focussed on the structure of the narrative world. Such models have been implemented in a wide variety of systems, often linked to characters’ goals and plans, where the goal of creating suspenseful stories is baked into the structure of each system. There is no portable, independently motivated idea of what makes a suspenseful story. Our approach is instead to take the phenomenon of suspense as the starting point. We extend an existing psychological model of narrative by Brewer and Lichtenstein (1982) which postulates suspense, curiosity and surprise as the fundamental elements of entertaining stories. We build a formal model of these phenomena using structures we call narrative threads. Narrative threads are a formal description of a reader’s expectations about what might happen next in a given story. Our model uses a measure for the imminence of the predicted conflict between narrative threads to create a suspense profile for a given story. We also identify two types of suspense: conflict-based and revelatory suspense. We tested the validity of our model by asking participants to give step- by-step self-reported suspense levels on reading online story variants. The results show that the normalised average scores of participants (N = 46) are in agreement with the values predicted by our model to a high level of statistical significance. Our model’s interface with storyworld knowledge is compatible with recent developments in automatic harvesting of world knowledge in the form of event chains such as Chambers and Jurafsky (2008). This means that it is in principle scalable. By disentangling suspense from specific narrative content and planning strategies, we arrive at a domain-independent model that can be reused within different narrative generation systems. We see our work as a signpost to encourage the further development of narrative models based on what we see as its fundamental ingredients

Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task

Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems

What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words, what kind of cognitive processes mediate sensing capability, and the formation of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory formation, beliefs and their revision, argument formation--- in domain-specific problem solving, or in regular activities of everyday living, working and simply going around in the environment? How can knowledge and reasoning about such capabilities, as exhibited by humans in particular problem contexts, be used as a model and benchmark for the development of collaborative cognitive (interaction) systems concerned with human assistance, assurance, and empowerment? We pose these questions in the context of a range of assistive technologies concerned with \emph{visuo-spatial perception and cognition} tasks encompassing aspects such as commonsense, creativity, and the application of specialist domain knowledge and problem-solving thought processes. Assistive technologies being considered include: (a) human activity interpretation; (b) high-level cognitive rovotics; (c) people-centred creative design in domains such as architecture & digital media creation, and (d) qualitative analyses geographic information systems. Computational narratives not only provide a rich cognitive basis, but they also serve as a benchmark of functional performance in our development of computational cognitive assistance systems. We posit that computational narrativisation pertaining to space, actions, and change provides a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a wide-range of problem-solving tasks and application areas where collaborative cognitive systems could serve an assistive and empowering function.Comment: 5 pages, research statement summarising recent publication

Computational Models (of Narrative) for Literary Studies

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1