Alípio Mario Jorge


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Text2Story Lusa: A Dataset for Narrative Analysis in European Portuguese News Articles
Sérgio Nunes | Alípio Mario Jorge | Evelin Amorim | Hugo Sousa | António Leal | Purificação Moura Silvano | Inês Cantante | Ricardo Campos
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Narratives have been the subject of extensive research across various scientific fields such as linguistics and computer science. However, the scarcity of freely available datasets, essential for studying this genre, remains a significant obstacle. Furthermore, datasets annotated with narratives components and their morphosyntactic and semantic information are even scarcer. To address this gap, we developed the Text2Story Lusa datasets, which consist of a collection of news articles in European Portuguese. The first datasets consists of 357 news articles and the second dataset comprises a subset of 117 manually densely annotated articles, totaling over 50 thousand individual annotations. By focusing on texts with substantial narrative elements, we aim to provide a valuable resource for studying narrative structures in European Portuguese news articles. On the one hand, the first dataset provides researchers with data to study narratives from various perspectives. On the other hand, the annotated dataset facilitates research in information extraction and related tasks, particularly in the context of narrative extraction pipelines. Both datasets are made available adhering to FAIR principles, thereby enhancing their utility within the research community.


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The place of ISO-Space in Text2Story multilayer annotation scheme
António Leal | Purificação Silvano | Evelin Amorim | Inês Cantante | Fátima Silva | Alípio Mario Jorge | Ricardo Campos
Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022

Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space.


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Developing a multilayer semantic annotation scheme based on ISO standards for the visualization of a newswire corpus
Purificação Silvano | António Leal | Fátima Silva | Inês Cantante | Fatima Oliveira | Alípio Mario Jorge
Proceedings of the 17th Joint ACL - ISO Workshop on Interoperable Semantic Annotation

In this paper, we describe the process of developing a multilayer semantic annotation scheme designed for extracting information from a European Portuguese corpus of news articles, at three levels, temporal, referential and semantic role labelling. The novelty of this scheme is the harmonization of parts 1, 4 and 9 of the ISO 24617 Language resource management - Semantic annotation framework. This annotation framework includes a set of entity structures (participants, events, times) and a set of links (temporal, aspectual, subordination, objectal and semantic roles) with several tags and attribute values that ensure adequate semantic and visual representations of news stories.