Challenges in Expanding Portuguese Resources: A View from Open Information Extraction
Not on the map. Why?
Abstract (arXiv)
Open Information Extraction (Open IE) is the task of extracting structured information from textual documents, independent of domain. While traditional Open IE methods were based on unsupervised approaches, recently, with the emergence of robust annotated datasets, new data-based approaches have been developed to achieve better results. These innovations, however, have focused mainly on the English language due to a lack of datasets and the difficulty of constructing such resources for other languages. In this work, we present a high-quality manually annotated corpus for Open Information Extraction in the Portuguese language, based on a rigorous methodology grounded in established semantic theories. We discuss the challenges encountered in the annotation process, propose a set of structural and contextual annotation rules, and validate our corpus by evaluating the performance of state-of-the-art Open IE systems. Our resource addresses the lack of datasets for Open IE in Portuguese and can support the development and evaluation of new methods and systems in this area.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
7 of 30 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 271
- 2019Portuguese Named Entity Recognition using BERT-CRFcited by 73
- 2018Neural Open Information Extractioncited by 50
- 2020What the [MASK]? Making Sense of Language-Specific BERT Modelscited by 44
- 2018WiRe57 : A Fine-Grained Benchmark for Open Information Extractioncited by 16
- 2019Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extractioncited by 15
Built on by
Not cited yet.