CQE: A Comprehensive Quantity Extractor
Satya Almasian Vivian Kazakova Philip Göldner Michael Gertz
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.