Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data
Roxana Girju, David Peng

TL;DR
This paper investigates how English perception verbs encode sensory experiences using distributional semantics and clustering, revealing differences in sensory spaces related to agentive and experiential uses across five senses.
Contribution
It introduces a data-driven method to analyze sensory verb meanings and distinguishes agentive versus experiential sensory spaces in natural language data.
Findings
Experiential verbs like 'to see' and 'to hear' show detached, logical sensory spaces.
Agentive verbs like 'to look' and 'to listen' are more intentional and intuitive.
The approach offers insights into the semantic organization of sensory language.
Abstract
In this study, we explore how language captures the meaning of words, in particular meaning related to sensory experiences learned from statistical distributions across texts. We focus on the most frequent perception verbs of English analyzed from an and Agentive vs. Experiential distinction across the five basic sensory modalities: Visual (to look vs. to see), Auditory (to listen vs. to hear), Tactile (to touch vs. to feel), Olfactory (to smell), and Gustatory (to taste). In this study we report on a data-driven approach based on distributional-semantic word embeddings and clustering models to identify and uncover the descriptor sensory spaces of the perception verbs. In the analysis, we identified differences and similarities of the generated descriptors based on qualitative and quantitative differences of the perceptual experience they denote. For instance, our results show that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsLanguage, Metaphor, and Cognition · Categorization, perception, and language · Advanced Text Analysis Techniques
