Year: 2023
Type: conference-paper
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.
Institution: Heidelberg University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389524189