不同生物事件的判别提取

Chen Li, Zhiqiang Rao, Xiangrong Zhang
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引用次数: 13

摘要

即使是一个简单的生物现象也可能引入一个复杂的分子相互作用网络。科学文献是传递这些网络知识的可靠资源之一。我们提出了LitWay,一个从文本中提取语义关系的系统。LitWay采用了一种混合方法,结合了基于规则的方法和基于机器学习的方法。在BioNLP-ST 2016的SeeDev任务测试中,以43.2%的f分取得了最先进的成绩,在所有参赛团队中排名第一。为了进一步揭示每个事件的语言特征,我们单独使用语法规则或机器学习以及两种方法的不同组合来测试系统。我们发现,由于文献中生物事件的复杂性,一种方法很难对所有语义关系类型达到良好的性能。
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LitWay, Discriminative Extraction for Different Bio-Events
Even a simple biological phenomenon may introduce a complex network of molecular interactions. Scientific literature is one of the trustful resources delivering knowledge of these networks. We propose LitWay, a system for extracting semantic relations from texts. LitWay utilizes a hybrid method that combines both a rule-based method and a machine learning-based method. It is tested on the SeeDev task of BioNLP-ST 2016, achieves the state-of-the-art performance with the F-score of 43.2%, ranking first of all participating teams. To further reveal the linguistic characteristics of each event, we test the system solely with syntactic rules or machine learning, and different combinations of two methods. We find that it is difficult for one method to achieve good performance for all semantic relation types due to the complication of bio-events in the literatures.
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