Quantities are essential in documents to describe factual information. They
are ubiquitous in application domains such as finance, business, medicine, and
science in general. Compared to other information extraction approaches,
interestingly only a few works exist that describe methods for a proper
extraction and representation of quantities in text. In this paper, we present
such a comprehensive quantity extraction framework from text data. It
efficiently detects combinations of values and units, the behavior of a
quantity (e.g., rising or falling), and the concept a quantity is associated
with. Our framework makes use of dependency parsing and a dictionary of units,
and it provides for a proper normalization and standardization of detected
quantities. Using a novel dataset for evaluation, we show that our open source
framework outperforms other systems and -- to the best of our knowledge -- is
the first to detect concepts associated with identified quantities. The code
and data underlying our framework are available at
this https URL

本文提出了一种基于依赖解析和单位字典的数量提取框架，它能够高效地检测数值和单位的组合、数量变化的行为以及数量所关联的概念，并对检测到的数量进行适当的标准化和规范化。使用新颖的数据集进行评估，我们展示了基于开源框架的方法优于其他系统，并且是目前为止首个检测到与已识别的数量相关概念的方法。