Yoshikawa Katsumasa, Riedel Sebastian, Hirao Tsutomu, Asahara Masayuki, Matsumoto Yuji
Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan.
J Biomed Semantics. 2011 Oct 6;2 Suppl 5(Suppl 5):S6. doi: 10.1186/2041-1480-2-S5-S6.
This paper presents a new approach to exploit coreference information for extracting event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations based on the concept of salience in discourse; (2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information.
本文提出了一种利用指代消解信息从生物医学文档中提取事件-论元（E-A）关系的新方法。该方法具有两个优点：（1）基于语篇显著性概念，它能够提取大量有价值的E-A关系；（2）利用指代关系的传递性，它使我们能够识别跨句子边界的E-A关系（交叉链接）。我们提出了两种基于指代消解的模型：一种基于支持向量机（SVM）分类器的流水线模型，以及一种联合马尔可夫逻辑网络（MLN）。我们在一个生物医学事件语料库上展示了这些模型的有效性。这两种模型均优于未使用指代消解信息的系统。当将这两种提出的模型相互比较时，联合MLN在使用金标准指代消解信息的情况下优于流水线SVM。
