Kogan Yacov, Collier Nigel, Pakhomov Serguei, Krauthammer Michael
Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA.
AMIA Annu Symp Proc. 2005;2005:410-4.
In this work, we introduce the concept of semantic role labeling to the medical domain. We report first results of porting and adapting an existing resource, Propbank, to the medical field. Propbank is an adjunct to Penn Treebank that provides semantic annotation of predicates and the roles played by their arguments. The main aim of this work is the applicability of the Propbank frame files to predicates typically encountered in the medical literature.
We analyzed a target corpus of 610,100 abstracts, which was selected by searching for publication type "case reports". From this target corpus, we randomly selected 10,000 sample abstracts to estimate the predicate distribution, and matched the predicates from this sample to the predicates in Propbank.
Of the 1998 unique verbs in our sample, 76% were represented in Propbank. This included the 40 most frequent verbs, which represented 49% of all predicate instances in our sample and which matched the Propbank usage in a study of representative sentences. We propose extensions to Propbank that handle medical predicates, which are not adequately covered by Propbank.
We believe that semantic role labeling using Propbank is a valid approach to capture predicate relations in the medical literature.
在本研究中，我们将语义角色标注的概念引入医学领域。我们报告了将现有资源Propbank移植并适配到医学领域的初步成果。Propbank是宾州树库的一个附属资源，它提供谓词及其论元所扮演角色的语义标注。这项工作的主要目标是使Propbank框架文件适用于医学文献中常见的谓词。
我们分析了一个由610100篇摘要组成的目标语料库，这些摘要通过搜索“病例报告”的出版物类型来选取。从这个目标语料库中，我们随机抽取10000篇样本摘要来估计谓词分布，并将该样本中的谓词与Propbank中的谓词进行匹配。
在我们样本中的1998个独特动词中，76%在Propbank中有对应。这包括40个最频繁出现的动词，它们占我们样本中所有谓词实例的49%，并且在一项代表性句子研究中与Propbank的用法相匹配。我们提议对Propbank进行扩展，以处理Propbank未充分涵盖的医学谓词。
我们认为使用Propbank进行语义角色标注是一种有效的方法，可以捕捉医学文献中的谓词关系。
