Linguistic studies in under-resourced languages pose additional challenges at various levels, including the automatic collection of examples, cases, and corpora construction. Several sophisticated applications, such as GATE (Cunningham, 2002), can be configured/adjusted/programmed by experts to automatically collect examples from the Web in any language. However, these applications are too complex and intricate to be operated, requiring, in some cases, skills in computer science. In this work, we present TELP, a tool that allows for the simplified expression of linguistic patterns to extract case studies automatically from World Wide Web sites. It is a straightforward application with an intuitive GUI and a quick learning curve, facilitating its broad use by researchers from different domains. In this paper, we describe the operational and technical aspects of TELP and some relatively recent and relevant use cases in the field of linguistic studies.

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.