# CQE: A Comprehensive Quantity Extractor

arXiv: https://arxiv.org/abs/2305.08853 · HTML: https://mlsanity.com/paper/2305.08853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Mining Measured Information from Text](https://mlsanity.com/paper/1505.01072/) (2015)
- [Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions](https://mlsanity.com/paper/2303.18103/) (2023)

## Built on by (most cited first, 4 of 4)

- [Quantitative Information Extraction from Humanitarian Documents](https://mlsanity.com/paper/2408.04941/) (2024)
- [Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia](https://mlsanity.com/paper/2503.14090/) (2025)
- [Toten: A Knowledge-Based System For Structure-Preserving Representation Of Physical Quantities And Technical Notation In Brazilian Portuguese](https://mlsanity.com/paper/2606.19626/) (2026)
- [Quantitative Evidence Mining for Plausibility-Aware Biomedical AI: A Narrative Review and Conceptual Framework](https://mlsanity.com/paper/2608.30393/) (2026)

## Abstract (arXiv)

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.