CQE: A Comprehensive Quantity Extractor
This addresses the need for better quantity extraction in domains like finance and medicine, but it is incremental as it builds on existing parsing and dictionary methods.
The paper tackles the problem of extracting and representing quantities from text, presenting a comprehensive framework that detects values, units, behavior, and associated concepts, and it outperforms other systems on a novel dataset.
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 https://github.com/vivkaz/CQE.