# Verb Physics: Relative Physical Knowledge of Actions and Objects

arXiv: https://arxiv.org/abs/1706.03799 · HTML: https://mlsanity.com/paper/1706.03799/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

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

- [CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge](https://mlsanity.com/paper/2109.01653/) (2021)
- [Representing Numbers in NLP: a Survey and a Vision](https://mlsanity.com/paper/2103.13136/) (2021)
- [PROST: Physical Reasoning of Objects through Space and Time](https://mlsanity.com/paper/2106.03634/) (2021)
- [Things not Written in Text: Exploring Spatial Commonsense from Visual Signals](https://mlsanity.com/paper/2203.08075/) (2022)
- [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://mlsanity.com/paper/2104.08161/) (2021)
- [Mathematical Language Models: A Survey](https://mlsanity.com/paper/2312.07622/) (2023)
- [Mitigating Temporal Misalignment by Discarding Outdated Facts](https://mlsanity.com/paper/2305.14824/) (2023)
- [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://mlsanity.com/paper/2109.04947/) (2021)
- [StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes](https://mlsanity.com/paper/2205.14036/) (2022)
- [CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering](https://mlsanity.com/paper/2211.03779/) (2022)
- [A Survey on Mathematical Reasoning and Optimization with Large Language Models](https://mlsanity.com/paper/2503.17726/) (2025)
- [Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge](https://mlsanity.com/paper/2102.05474/) (2021)
- [Reasoning about Actions over Visual and Linguistic Modalities: A Survey](https://mlsanity.com/paper/2207.07568/) (2022)
- [Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge](https://mlsanity.com/paper/2112.08583/) (2021)
- [NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge](https://mlsanity.com/paper/2305.04978/) (2023)
- [What's the best place for an AI conference, Vancouver or ______: Why completing comparative questions is difficult](https://mlsanity.com/paper/2104.01940/) (2021)
- [VIPHY: Probing "Visible" Physical Commonsense Knowledge](https://mlsanity.com/paper/2209.07000/) (2022)
- [Probing Physical Reasoning with Counter-Commonsense Context](https://mlsanity.com/paper/2306.02258/) (2023)
- [Do Language Models Understand Measurements?](https://mlsanity.com/paper/2210.12694/) (2022)
- [From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning](https://mlsanity.com/paper/2310.18364/) (2023)
- [Inferring spatial relations from textual descriptions of images](https://mlsanity.com/paper/2102.00997/) (2021)
- [Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships](https://mlsanity.com/paper/2404.19432/) (2024)
- [GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models](https://mlsanity.com/paper/2509.19593/) (2025)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models](https://mlsanity.com/paper/2406.02864/) (2024)
- [Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge](https://mlsanity.com/paper/2408.16937/) (2024)

## Abstract (arXiv)

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, "Tyler entered his house" implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.