Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data

The success of grounded language acquisition using perceptual data (e.g., in robotics) is affected by the complexity of both the perceptual concepts being learned and the language describing those concepts. We present methods for analyzing this complexity, using both visual features and entropy-base...

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Main Authors: Nisha Pillai, Cynthia Matuszek, Francis Ferraro
Format: Article
Language:English
Published: LibraryPress@UF 2021-04-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
Online Access:https://journals.flvc.org/FLAIRS/article/view/128450
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author Nisha Pillai
Cynthia Matuszek
Francis Ferraro
author_facet Nisha Pillai
Cynthia Matuszek
Francis Ferraro
author_sort Nisha Pillai
collection DOAJ
description The success of grounded language acquisition using perceptual data (e.g., in robotics) is affected by the complexity of both the perceptual concepts being learned and the language describing those concepts. We present methods for analyzing this complexity, using both visual features and entropy-based evaluation of sentences. Our work illuminates core, quantifiable statistical differences in how language is used to describe different traits of objects, and the visual representation of those objects. The methods we use provide an additional analytical tool for research in perceptual language learning.
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institution DOAJ
issn 2334-0754
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series Proceedings of the International Florida Artificial Intelligence Research Society Conference
spelling doaj-art-2728577a838b4f7cbb7a4e2d3892d1f22025-08-20T03:05:50ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622021-04-013410.32473/flairs.v34i1.12845062844Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language DataNisha Pillai0Cynthia Matuszek1Francis Ferraro2University of Maryland, Baltimore CountyUniversity of Maryland, Baltimore CountyUniversity of Maryland, Baltimore CountyThe success of grounded language acquisition using perceptual data (e.g., in robotics) is affected by the complexity of both the perceptual concepts being learned and the language describing those concepts. We present methods for analyzing this complexity, using both visual features and entropy-based evaluation of sentences. Our work illuminates core, quantifiable statistical differences in how language is used to describe different traits of objects, and the visual representation of those objects. The methods we use provide an additional analytical tool for research in perceptual language learning.https://journals.flvc.org/FLAIRS/article/view/128450roboticsnatural language processingmultimodalcomplexity
spellingShingle Nisha Pillai
Cynthia Matuszek
Francis Ferraro
Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference
robotics
natural language processing
multimodal
complexity
title Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
title_full Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
title_fullStr Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
title_full_unstemmed Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
title_short Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
title_sort measuring perceptual and linguistic complexity in multilingual grounded language data
topic robotics
natural language processing
multimodal
complexity
url https://journals.flvc.org/FLAIRS/article/view/128450
work_keys_str_mv AT nishapillai measuringperceptualandlinguisticcomplexityinmultilingualgroundedlanguagedata
AT cynthiamatuszek measuringperceptualandlinguisticcomplexityinmultilingualgroundedlanguagedata
AT francisferraro measuringperceptualandlinguisticcomplexityinmultilingualgroundedlanguagedata