Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA.
J Am Med Inform Assoc. 2012 Sep-Oct;19(5):867-74. doi: 10.1136/amiajnl-2011-000766. Epub 2012 Jun 16.
This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity.
The task organizers provided progress notes and discharge summaries that were annotated with the markables of treatment, problem, test, person, and pronoun. We used a multi-pass sieve algorithm that applies deterministic rules in the order of preciseness and simultaneously gathers information about the entities in the documents. Our system, MedCoref, also uses a state-of-the-art machine learning framework as an alternative to the final, rule-based pronoun resolution sieve.
The best system that uses a multi-pass sieve has an overall score of 0.836 (average of B(3), MUC, Blanc, and CEAF F score) for the training set and 0.843 for the test set.
A supervised machine learning system that typically uses a single function to find coreferents cannot accommodate irregularities encountered in data especially given the insufficient number of examples. On the other hand, a completely deterministic system could lead to a decrease in recall (sensitivity) when the rules are not exhaustive. The sieve-based framework allows one to combine reliable machine learning components with rules designed by experts.
Using relatively simple rules, part-of-speech information, and semantic type properties, an effective coreference resolution system could be designed. The source code of the system described is available at https://sourceforge.net/projects/ohnlp/files/MedCoref.
本文介绍 Mayo 诊所为 2011 年 i2b2/VA/Cincinnati 共享任务第 1C 轨道提交的共指消解系统。该任务的目标是构建一个能够将对应于同一实体的可标记项链接起来的系统。
任务组织者提供了带有治疗、问题、测试、人员和代词标记项的进展记录和出院小结。我们使用了一种多步筛选算法，该算法按照精确性的顺序应用确定性规则，同时收集文档中实体的信息。我们的系统 MedCoref 还使用了最先进的机器学习框架作为最终基于规则的代词消解筛选器的替代方案。
使用多步筛选器的最佳系统在训练集上的总分为 0.836（B(3)、MUC、Blanc 和 CEAF F 分数的平均值），在测试集上的总分为 0.843。
一个典型地使用单一函数来寻找共指项的监督机器学习系统无法适应数据中的不规则性，尤其是在示例数量不足的情况下。另一方面，完全确定性的系统可能会导致规则不详尽时召回率（灵敏度）下降。基于筛选器的框架允许将可靠的机器学习组件与专家设计的规则结合起来。
使用相对简单的规则、词性信息和语义类型属性，可以设计出有效的共指消解系统。描述的系统的源代码可在 https://sourceforge.net/projects/ohnlp/files/MedCoref 上获得。
