Narrative Recomposition in the Context of Digital Reading

In any creative process, the tools one uses have an immediate influence on the shape of the final artwork. However, while the digital revolution has redefined core values in most creative domains over the last few decades, its impact on literature remains limited. This thesis explores the relevance of digital tools for several aspects of novels writing by focusing on two research questions: Is it possible for an author to edit better novels out of already published ones, given the access to adapted tools? And, will authors change their way of writing when they know how they are being read? This thesis is a multidisciplinary participatory study, actively involving the Swiss novelist Daniel de Roulet, to construct measures, visualizations, and digital tools aimed at leveraging the process of dynamic reordering of narrative material, similar to how one edits a video footage. We developed and tested various text analysis and visualization tools, the results of which were interpreted and used by the author to recompose a family saga out of material he has been writing for twenty-four years. Based on this research, we released Saga+, an online editing, publishing, and reading tool. The platform was handed out to third parties to improve existing writings, making new novels available to the public as a result. While many researchers have studied the structuration of texts either through global statistical features or micro-syntactic analyses, we demonstrate that by allowing visualization and interaction at an intermediary level of organisation, authors can manipulate their own texts in agile ways. By integrating readersâ traces into this newly revealed structure, authors can start to approach the question of optimizing their writing processes in ways that are similar to what is being practiced in other media industries. The introduction of tools for optimal composition opens new avenues for authors, as well as a controversial debate regarding the future of literature.

Automatic target validation based on neuroscientific literature mining for tractography

Jocelyne Bloch, Jean-Cédric Chappelier, Bogdan Draganski, Sean Lewis Hill, Renaud Luc Richardet, David Slater, Xavier Vasques

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Frontiers Research Foundation2015

Automatic target validation based on neuroscientific literature mining for tractography

Jocelyne Bloch, Jean-Cédric Chappelier, Bogdan Draganski, Sean Lewis Hill, Renaud Luc Richardet, David Slater, Xavier Vasques

Frontiers Research Foundation2015