CQE: A Comprehensive Quantity Extractor

Satya Almasian, Vivian Kazakova, Philipp Göldner, Michael Gertz

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: Resources and Evaluation

Keywords: quantities, quantity extraction, information extraction

Abstract:

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.

Submission Number: 64