# How Large Are Lions? Inducing Distributions over Quantitative Attributes

arXiv: https://arxiv.org/abs/1906.01327 · HTML: https://mlsanity.com/paper/1906.01327/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 26 of 26)

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- [NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models](https://mlsanity.com/paper/2406.02864/) (2024)
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## Abstract (arXiv)

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as "Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.