# Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia

arXiv: https://arxiv.org/abs/2503.14090 · HTML: https://mlsanity.com/paper/2503.14090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models](https://mlsanity.com/paper/2005.00683/) (2020)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers](https://mlsanity.com/paper/1805.08154/) (2018)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [An Empirical Investigation of Contextualized Number Prediction](https://mlsanity.com/paper/2011.07961/) (2020)
- [A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach](https://mlsanity.com/paper/2211.10018/) (2022)
- [Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting](https://mlsanity.com/paper/1809.05356/) (2018)
- [How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions](https://mlsanity.com/paper/1609.00070/) (2016)
- [CQE: A Comprehensive Quantity Extractor](https://mlsanity.com/paper/2305.08853/) (2023)
- [Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction](https://mlsanity.com/paper/2405.10288/) (2024)

## Abstract (arXiv)

To cope with the large number of publications, more and more researchers are automatically extracting data of interest using natural language processing methods based on supervised learning. Much data, especially in the natural and engineering sciences, is quantitative, but there is a lack of datasets for identifying quantities and their context in text. To address this issue, we present two large datasets based on Wikipedia and Wikidata: Wiki-Quantities is a dataset consisting of over 1.2 million annotated quantities in the English-language Wikipedia. Wiki-Measurements is a dataset of 38,738 annotated quantities in the English-language Wikipedia along with their respective measured entity, property, and optional qualifiers. Manual validation of 100 samples each of Wiki-Quantities and Wiki-Measurements found 100% and 84-94% correct, respectively. The datasets can be used in pipeline approaches to measurement extraction, where quantities are first identified and then their measurement context. To allow reproduction of this work using newer or different versions of Wikipedia and Wikidata, we publish the code used to create the datasets along with the data.