Information Extraction (IE) from scientific texts can be used to guide
readers to the central information in scientific documents. But narrow IE
systems extract only a fraction of the information captured, and Open IE
systems do not perform well on the long and complex sentences encountered in
scientific texts. In this work we combine the output of both types of systems
to achieve Semi-Open Relation Extraction, a new task that we explore in the
Biology domain. First, we present the Focused Open Biological Information
Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow
scientific IE system to extract trade-off relations and arguments that are
central to biology texts. We then run both the narrow IE system and a
state-of-the-art Open IE system on a corpus of 10k open-access scientific
biological texts. We show that a significant amount (65%) of erroneous and
uninformative Open IE extractions can be filtered using narrow IE extractions.
Furthermore, we show that the retained extractions are significantly more often
informative to a reader.

本研究针对科技文本的开放式关系抽取技术不足问题，将窄 IE 系统和开放式 IE 系统结合，提出了一种新的任务 —— 半开放式关系抽取，并应用于生物领域。研究在 FOBIE 数据集上，训练出能够提取生物学文本中重要关系和论述的最先进的窄 IE 系统，并在 10k 篇生物学开放式科技文本上运行窄 IE 系统和开放式 IE 系统，取得了过滤出 65% 的错误和无用开放式 IE 提取结果，并且发现保留下来的提取结果对于读者更有信息价值。