Supervised Learning Based Hypothesis Generation from Biomedical Literature.
Supervised Learning Based Hypothesis Generation from Biomedical Literature.
复制标题
基于生物医学文献的监督学习假设生成。
DOI：
10.1155/2015/698527
复制
发表时间：
2015
影响因子：
--
通讯作者：
Lin H
中科院分区：
文献类型：
--
作者：
Sang S;Yang Z;Li Z;Lin H
目前，生物医学文献的数量正以爆炸式的速度增长，并且在这些文献中有许多有用的知识未被发现。研究人员可以通过挖掘这些作品形成生物医学假说。在本文中，我们提出了一种基于监督学习的方法，从生物医学文献中生成假设。该方法将传统的基于经典ABC模型的假设生成过程分解为基于监督学习方法的AB模型和BC模型。与SemRep等基于概念共现和语法工程的方法相比，基于机器学习的模型通常可以在文本信息提取（IE）中获得更好的性能。然后，通过结合两个模型，该方法重建ABC模型，并从文献中生成生物医学假设。在三个经典Swanson假设上的实验结果表明，该方法优于SemRep系统.
Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.