Theodoropoulos Christos, Coman Andrei Catalin, Henderson James, Moens Marie-Francine
Computer Science Department, KU Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium.
Natural Language Understanding group, Idiap Research Institute, Rue Marconi 19, 1920, Martigny, Switzerland.
BMC Bioinformatics. 2025 Sep 1;26(1):225. doi: 10.1186/s12859-025-06187-0.
Knowledge discovery in scientific literature is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential to employ automated methods for knowledge extraction and processing. Finding the right balance between the level of supervision and the effectiveness of models poses a significant challenge. While supervised techniques generally result in better performance, they have the major drawback of demanding labeled data. This requirement is labor-intensive, time-consuming, and hinders scalability when exploring new domains.
In this context, our study addresses the challenge of identifying semantic relationships between biomedical entities (e.g., diseases, proteins, medications) in unstructured text while minimizing dependency on supervision. We introduce a suite of unsupervised algorithms based on dependency trees and attention mechanisms and employ a range of pointwise binary classification methods. Transitioning from weakly supervised to fully unsupervised settings, we assess the methods' ability to learn from data with noisy labels. The evaluation on four biomedical benchmark datasets explores the effectiveness of the methods, demonstrating their potential to enable scalable knowledge discovery systems less reliant on annotated datasets.
Our approach tackles a central issue in knowledge discovery: balancing performance with minimal supervision which is crucial to adapting models to varied and changing domains. This study also investigates the use of pointwise binary classification techniques within a weakly supervised framework for knowledge discovery. By gradually decreasing supervision, we assess the robustness of these techniques in handling noisy labels, revealing their capability to shift from weakly supervised to entirely unsupervised scenarios. Comprehensive benchmarking offers insights into the effectiveness of these techniques, examining how unsupervised methods can reliably capture complex relationships in biomedical texts. These results suggest an encouraging direction toward scalable, adaptable knowledge discovery systems, representing progress in creating data-efficient methodologies for extracting useful insights when annotated data is limited.
科学文献中的知识发现受到出版物数量不断增加以及广泛注释数据稀缺的阻碍。为应对信息过载的挑战，采用自动化的知识提取和处理方法至关重要。在监督水平和模型有效性之间找到恰当平衡构成了重大挑战。虽然监督技术通常能带来更好的性能，但它们存在需要标记数据这一主要缺点。这一要求劳动强度大、耗时，并且在探索新领域时会阻碍可扩展性。
在此背景下，我们的研究解决了在非结构化文本中识别生物医学实体（如疾病、蛋白质、药物）之间语义关系的挑战，同时尽量减少对监督的依赖。我们引入了一套基于依存树和注意力机制的无监督算法，并采用了一系列点式二元分类方法。从弱监督设置过渡到完全无监督设置，我们评估了这些方法从带有噪声标签的数据中学习的能力。在四个生物医学基准数据集上的评估探索了这些方法的有效性，证明了它们在实现对注释数据集依赖较少的可扩展知识发现系统方面的潜力。
我们的方法解决了知识发现中的一个核心问题：在最小化监督的情况下平衡性能，这对于使模型适应不同且不断变化的领域至关重要。本研究还调查了在弱监督框架内使用点式二元分类技术进行知识发现的情况。通过逐步减少监督，我们评估了这些技术在处理噪声标签时的稳健性，揭示了它们从弱监督场景转向完全无监督场景的能力。全面的基准测试提供了对这些技术有效性的见解，考察了无监督方法如何可靠地捕捉生物医学文本中的复杂关系。这些结果为可扩展、适应性强的知识发现系统指明了一个令人鼓舞的方向，代表了在创建数据高效方法以在注释数据有限时提取有用见解方面取得的进展。
