Abstract
Owing to the exponential rise in the electronic medical records, information extraction in this domain is becoming an important area of research in recent years. Relation extraction between the medical concepts such as medical problem, treatment, and test etc. is also one of the most important tasks in this area. In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence. Instead of relying on many handcrafted features and the whole sequence of tokens present in a sentence, our system relies only on the SDP between the target entities. For every pair of entities, the system takes only the words in the SDP, their dependency labels, Part-of-Speech information and the types of the entities as the input. We develop a dependency parser for extracting dependency information. We perform our experiments on the benchmark i2b2 dataset for clinical relation extraction challenge 2010. Experimental results show that our system outperforms the existing systems.
Abstract (translated)
近年来,由于电子病历的指数增长,这一领域的信息提取正成为一个重要的研究领域。医学问题、治疗、检验等医学概念之间的关系提取也是这一领域最重要的任务之一。本文提出了一种基于句子依赖分析树生成的最短依赖路径(SDP)的有效关系提取系统。我们的系统不依赖于许多手工制作的特性和语句中出现的整个令牌序列,而只依赖于目标实体之间的SDP。对于每对实体,系统只接受SDP中的单词、它们的依赖标签、部分语音信息和实体类型作为输入。我们开发了一个依赖性解析器来提取依赖性信息。我们在2010年临床关系提取挑战的基准I2B2数据集上进行实验。实验结果表明,该系统优于现有系统。
URL
https://arxiv.org/abs/1903.09941