Abstract

Abstract
Narratives are used to convey information and are an important way of understanding the world through information sharing. With the increasing development in Natural Language Processing and Artificial Intelligence, it becomes relevant to explore new techniques to extract, process, and visualize narratives. Narrative visualization tools enable a news story reader to have a different perspective from the traditional format, allowing it to be presented in a schematic way, using representative symbols to summarize it. We propose a new narrative visualization approach using icons to represent important narrative elements. The proposed visualization is integrated in Brat2Viz, a narrative annotation visualization tool that implements a pipeline that transforms text annotations into formal representations leading to narrative visualizations. To build the icon visualization, we present a narrative element extraction process that uses automatic sentence extraction, automatic translation methods, and an algorithm that determines the actors' most adequate descriptions. Then, we introduce a method to create an icon dictionary, with the ability to automatically search for icons. Furthermore, we present a critical analysis and user-based evaluation of the results resorting to the responses collected in two separate surveys.

Abstract
Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.

Abstract

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic illness that inflicts severe symptoms after the onset occurs. Age of onset represents the moment a patient starts to experience the symptoms of a disease. An accurate prediction of this event can improve clinical and operational guidelines that define the work of doctors, nurses, and operational staff. In this work, we transform family trees into compact vectors, that is, embeddings, and handle these as input features to predict the age of onset of patients with TTR-FAP. Our purpose is to evaluate how information present in genealogical trees can be transformed and used to improve a regression-based setting for TTR-FAP age of onset prediction. Our results show that by combining manual and graph-embeddings features there is a decrease in the mean prediction error when there is less information regarding a patient's family. With this work, we open the way for future work in representation learning for genealogical data, enabling a more effective exploitation of machine learning approaches.

Abstract
[No abstract available]