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Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming ...
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The interaction between cognitive ease and informativeness shapes the lexicons of natural languages ...
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Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Abstract:
Often we can use different names to refer to the same object (e.g., pony vs. horse) and naming choices vary among people. In the present study we explore factors that affect naming variation for visually presented objects. We analyse a large dataset of object naming with realistic images and focus on two factors: (a) the visual typicality of objects and their context for the names used by human annotators and (b) the lexical frequency of these names. We use a novel computational approach to estimate visual typicality by calculating the visual similarity of a given object (or context) and the average visual information of other objects which were given the same name (in an independent dataset). In difference to previous studies, we not only consider the name used by most annotators for a given object (top name) but explore also the role of the second most frequently used name (alternative name). Our results show that naming variation decreases the more typical an object is for its top name and the higher the lexical frequency of this name. For alternative names the opposite is found. Context typicality does not show a general effect in our analysis. Overall our results show that visual and lexical characteristics relating to name candidates beyond the top name are informative for predicting variability in object naming. On a methodological level, our results demonstrate the potential of using large scale datasets with realistic images in conjunction with computational methods to inform models of human object naming.
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Keyword:
Computational Linguistics; context typicality; lexical frequency; name agreement; name variability; object naming; object typicality; visual typicality
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URL: https://scholarworks.umass.edu/scil/vol5/iss1/26
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1245&context=scil
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The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Probing the linguistic knowledge of word embeddings: A case study on colexification
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In: http://etd.adm.unipi.it/theses/available/etd-06212021-172428/ (2021)
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Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks ...
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Existential presupposition projection from none? : an experimental investigation
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Diagnosing truth, interactive sincerity, and depictive sincerity
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In: Semantics and Linguistic Theory; Proceedings of SALT 23; 358-375 ; 2163-5951 (2013)
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Only, at least, more, and less
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In: LSA Annual Meeting Extended Abstracts; Vol 4: LSA Annual Meeting Extended Abstracts 2013; 7:1-5 ; 2377-3367 (2013)
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Deep daxes: mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
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Modeling word interpretation with deep language models: the interaction between expectations and lexical information
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