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

Cognitive Interpretation of Everyday Activities - Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation

We position a narrative-centred computational model for high-level knowledge representation and reasoning in the context of a range of assistive technologies concerned with visuo-spatial perception and cognition tasks. Our proposed narrative model encompasses aspects such as space, events, actions, change, and interaction from the viewpoint of commonsense reasoning and learning in large-scale cognitive systems. The broad focus of this paper is on the domain of human-activity interpretation in smart environments, ambient intelligence etc. In the backdrop of a smart meeting cinematography domain, we position the proposed narrative model, preliminary work on perceptual narrativisation, and the immediate outlook on constructing general-purpose open-source tools for perceptual narrativisation

Communicating with Culture: How Humans and Machines Detect Narrative Elements

To understand how people communicate, we must understand how they leverage shared stories and all the knowledge, information, and associations contained within those stories. I examine three classes of narrative elements that convey a wealth of cultural knowledge: Propp\u27s morphology, motifs, and discourse structure. Propp\u27s morphology communicates how roles and actions drive a narrative forward; motifs fill those roles and actions with specific, remarkable events; discourse groups these into a coherent structure to convey a point. My thesis has three aims: first, to demonstrate that people can reliably detect and identify all three of these narrative elements; second, to develop automatic detectors for discourse and motifs; third, to demonstrate the deep relation between these narrative elements and other theories of narrative structure and knowledge representation that I refer to as the \textit{continuum of communication}. The first step of my work answers two key questions about Propp\u27s morphology by demonstrating the reliability of annotators applying Propp\u27s scheme across a variety of experiments, in a double-blind annotation study. Additionally, I demonstrate a shortcoming in Propp\u27s scheme, demonstrating areas in which there are elements present in the folktales he analyzed that are not part of his morphology. The second step of my work, showing that people familiar with motifs can reliably detect when they are being used to share information and associations, approaches this problem by performing a large-scale annotation study of 21,000 examples into four categories performed by three pairs of annotators over a period of 11 weeks. I show that, in a double-blind annotation study, people familiar with the motifs had a moderate to high degree of agreement, demonstrating the reliability of humans at this task. The third step demonstrates the reliability of applying a theory of news discourse structure to news articles via a double-blind annotation study and, using the results of this annotation, demonstrate a preliminary detector of the news discourse function of paragraphs in news articles. The fourth step of my work, detecting motific usage automatically, consists of a large-scale pipeline that achieves moderate performance. This pipeline is the first work towards automatically detecting motific usage of motifs and beats out simple baselines while comparing favorably too and generalizing better than a simple neural network baseline system. Additionally, the pipeline uses explainable features that can be used in future work to further develop our understanding of how humans automatically detect motifs. Finally, I describe an exploration of the broader scope of narrative elements that communicate information between individuals who share a cultural or sub-cultural background. This work is based off of a small-scale, in-lab annotation of posts from the “incel” subculture, a niche internet community with extremist elements and, at times, disturbing content. This small annotation has revealed a complex landscape encompassing fourteen categories, more than three times the number of elements as the large-scale annotation, many of which resemble the moving parts of other theories on narrative structure and cognition, including Vladimir Propp\u27s morphology of folktales and Silvan Tomkins\u27 script theory. I describe these relations and provide a rough continuum of the landscape of narrative communication

Automatic Extraction of Narrative Structure from Long Form Text

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65. Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator identifies themselves) and diegesis (how involved in the story’s action is the narrator) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This allows for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These tasks are impossible when the computer can’t distinguish between which stories are told in what spans of text. There are two key contributions in my work: 1) my identification of features that accurately extract elements of narrative structure and 2) the gold-standard data and reports generated from running annotation studies on identifying narrative structure

2013 Workshop on Computational Models of Narrative: CMN'13, 4-6 August 2013, Hamburg, Germany

Frontmatter, Table of Contents, Preface, Workshop Organizatio

Teaching Classics in the Digital Age

The papers and videos presented here are the result of the international conference 'Teaching Classics in the Digital Age' held online on the 15 and 16 June 2020. As digital media provide new possibilities for teaching and outreach in Classics, the conference 'Teaching Classics in the Digital Age' aimed at presenting current approaches to digital teaching and sharing best practices by bringing together different projects and practitioners from all fields of Classics (including Classical Archaeology, Greek and Latin Studies and Ancient History). Furthermore, it aimed at starting a discussion about principles, problems and the future of teaching Classics in the 21st century within and beyond its single fields