Measurement Extraction with Natural Language Processing: A Review

Abstract

Quantitative data is important in many domains. Information extraction methods draw structured data from documents. However, the extraction of quantities and their contexts has received little attention in the history of information extraction. In this review, an overview of prior work on measurement extraction is presented. We describe different approaches to measurement extraction and outline the challenges posed by this task. The review concludes with an outline of potential future research. Research strains in measurement extraction tend to be isolated and lack a common terminology. Improvements in numerical reasoning, more extensive datasets, and the consideration of wider contexts may lead to significant improvements in measurement extraction.