Automatic recognition of speech, thought, and writing representation in German narrative texts

This article presents the main results of a project, which explored ways to recognize and classify a narrative feature—speech, thought, and writing representation (ST&WR)—automatically, using surface information and methods of computational linguistics. The task was to detect and distinguish four types—direct, free indirect, indirect, and reported ST&WR—in a corpus of manually annotated German narrative texts. Rule-based as well as machine-learning methods were tested and compared. The results were best for recognizing direct ST&WR (best F1 score: 0.87), followed by indirect (0.71), reported (0.58), and finally free indirect ST&WR (0.40). The rule-based approach worked best for ST&WR types with clear patterns, like indirect and marked direct ST&WR, and often gave the most accurate results. Machine learning was most successful for types without clear indicators, like free indirect ST&WR, and proved more stable. When looking at the percentage of ST&WR in a text, the results of machine-learning methods always correlated best with the results of manual annotation. Creating a union or intersection of the results of the two approaches did not lead to striking improvements. A stricter definition of ST&WR, which excluded borderline cases, made the task harder and led to worse results for both approaches

An XML Annotation Schema for speech, thought and writing representation

This contribution presents an XML Schema for annotating a high level narratological category: speech, thought and writing representation (ST&WR). It focusses on two aspects: Firstly, the original Schema is presented as an example for the challenge to encode a narrative feature in a structured and flexible way and secondly, ways of adapting this Schema to TEI are considered, in Order to make it usable for other, TEI-based projects

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Automatic Annotation of Direct Speech in Written French Narratives

The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.Comment: 9 pages, ACL 202

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

Computing the Affective-Aesthetic Potential of Literary Texts

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Graphomania: Composing Subjects in Late-Victorian Gothic Fiction and Technology

This dissertation explores the varied phenomena of “automatic writing” in Victorian Gothic fiction, reading the genre’s fascination with the irrepressible signifying practices of the body in light of the medical, criminological and scientific discourses that underwrite the “scriptural economy” of the late nineteenth century with their own arsenal of automatic writing machines. I have titled the project Graphomania, and I consider the term a keyword of late-Victorian culture—one that names a distinctly Victorian pathology of compulsive writing, but that alludes also to the widespread epistemic hope that writing could render objectively the internal and subjective experiences of individuals. In a chapter devoted to Victorian graphomania and the three studies that follow (graphology in Jekyll and Hyde, retinal photography in The Beetle, and phonography in Dracula), the project is particularly interested in convergences and correspondences between graphical machines and human bodies. In this study, Victorian technology and Gothic literature emerge as twin registers of the divided self, joined in their shared strategy of externalizing conflicts traditionally understood as invisible processes, but also in the consequent tendency of each uncanny text to expose its ghostly remainders and excesses in the process of trying to contain them

Film policy and the emergence of the cross-cultural: exploring crossover cinema in Flanders (Belgium)

With several films taking on a cross-cultural character, a certain ‘crossover trend’ may be observed within the recent upswing of Flemish cinema (a subdivision of Belgian cinema). This trend is characterized by two major strands: first, migrant and diasporic filmmakers finally seem to be emerging, and second, several filmmakers tend to cross the globe to make their films, hereby minimizing links with Flemish indigenous culture. While paying special attention to the crucial role of film policy in this context, this contribution further investigates the crossover trend by focusing on Turquaze (2010, Kadir Balci) and Altiplano (2009, Peter Brosens & Jessica Woodworth)