Open Information Extraction (OIE) is the task of the unsupervised creation of
structured information from text. OIE is often used as a starting point for a
number of downstream tasks including knowledge base construction, relation
extraction, and question answering. While OIE methods are targeted at being
domain independent, they have been evaluated primarily on newspaper,
encyclopedic or general web text. In this article, we evaluate the performance
of OIE on scientific texts originating from 10 different disciplines. To do so,
we use two state-of-the-art OIE systems applying a crowd-sourcing approach. We
find that OIE systems perform significantly worse on scientific text than
encyclopedic text. We also provide an error analysis and suggest areas of work
to reduce errors. Our corpus of sentences and judgments are made available.

该论文使用众包方法评估两种最先进的开放信息抽取系统在 10 个不同学科的科学文本上的性能，发现 OIE 系统在科学文本上的表现明显劣于百科全书文本，在提供错误分析并建议减少错误的领域的同时，提出了一个句子和判断的语料库。