Abstract
Knowledge discovery 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 and 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) 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 biomedical benchmark datasets explores the effectiveness of the methods. Our approach tackles a central issue in knowledge discovery: balancing performance with minimal supervision. By gradually decreasing supervision, we assess the robustness of pointwise binary classification 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, suggesting an encouraging direction toward adaptable knowledge discovery systems, representing progress in creating data-efficient methodologies for extracting useful insights when annotated data is limited.
Abstract (translated)
知识发现受到出版物数量不断增加以及详尽标注数据稀缺的阻碍。为应对信息过载的挑战，必须采用自动化的方法进行知识提取和处理。在监督水平与模型效果之间找到平衡点是一项重大挑战。虽然监督技术通常能获得更好的性能表现，但它们的主要缺点是需要大量的标注数据。这一需求既耗时又费力，并且限制了当探索新领域时系统的可扩展性。在此背景下，我们的研究致力于识别无结构文本中生物医学实体（如疾病、蛋白质）之间的语义关系，同时尽量减少对监督的依赖。 为此，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进行评估。通过从弱监督环境过渡到完全无监督设置，我们评估了这些方法在处理带有噪声标签数据时的学习能力。我们在生物医学基准数据集上的实验探讨了这些方法的有效性。 我们的方法解决了知识发现中的一个核心问题：即如何在最小化监督的前提下保持性能的高效性。通过逐步减少监督力度，我们评估点式二元分类技术在处理含噪标签方面的能力，并揭示它们从弱监督环境向完全无监督场景转变的可能性。全面基准测试提供了这些技术效果的洞见，暗示了一个鼓舞人心的方向——朝着适应性强的知识发现系统迈进，这代表着当标注数据有限时高效提取有用信息的方法论进步。
URL
https://arxiv.org/abs/2504.09582
