# Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context

arXiv: https://arxiv.org/abs/2203.05648 · HTML: https://mlsanity.com/paper/2203.05648/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 120 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021)
- [MERLOT: Multimodal Neural Script Knowledge Models](https://mlsanity.com/paper/2106.02636/) (2021)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World](https://mlsanity.com/paper/2106.00188/) (2021)
- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [Language (Re)modelling: Towards Embodied Language Understanding](https://mlsanity.com/paper/2005.00311/) (2020)
- [(Re)construing Meaning in NLP](https://mlsanity.com/paper/2005.09099/) (2020)
- [RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)](https://mlsanity.com/paper/2105.13266/) (2021)
- [Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords](https://mlsanity.com/paper/2109.11491/) (2021)
- [Incorporating Terminology Constraints in Automatic Post-Editing](https://mlsanity.com/paper/2010.09608/) (2020)

## Built on by (most cited first, 3 of 3)

- [Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings](https://mlsanity.com/paper/2503.06980/) (2025)
- [Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention](https://mlsanity.com/paper/2510.01652/) (2025)
- [Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective](https://mlsanity.com/paper/2211.02812/) (2022)

## Abstract (arXiv)

Most large language models are trained on linguistic input alone, yet humans appear to ground their understanding of words in sensorimotor experience. A natural solution is to augment LM representations with human judgments of a word's sensorimotor associations (e.g., the Lancaster Sensorimotor Norms), but this raises another challenge: most words are ambiguous, and judgments of words in isolation fail to account for this multiplicity of meaning (e.g., "wooden table" vs. "data table"). We attempted to address this problem by building a new lexical resource of contextualized sensorimotor judgments for 112 English words, each rated in four different contexts (448 sentences total). We show that these ratings encode overlapping but distinct information from the Lancaster Sensorimotor Norms, and that they also predict other measures of interest (e.g., relatedness), above and beyond measures derived from BERT. Beyond shedding light on theoretical questions, we suggest that these ratings could be of use as a "challenge set" for researchers building grounded language models.