Previous research has speculated that semantic diversity and lexical ambiguity may be closely related constructs. Our research sought to test this claim in respect of the semantic diversity measure proposed by Hoffman et al. (2013). To this aim, we replicated the procedure described by Hoffman et al. (2013) for computing multidimensional representations of contextual information using Latent Semantic Analysis, and from these we derived semantic diversity values for 28,555 words. We then replicated the faciliatory effect of semantic diversity on word recognition using existing data resources and observed this effect to be greater for low frequency words. Yet, we found no relationship between this measure and lexical ambiguity effects in word recognition. Further analysis of the LSA-based contextual representations used to compute Hoffman et al.’s (2013) measure of semantic diversity revealed that they do not capture the distinct meanings of ambiguous words. Instead, these contextual representations appear to capture general information about the topics and types of written material in which words occur. These analyses suggest that the semantic diversity metric previously proposed by Hoffman et al. (2013) facilitates word recognition because high diversity words are likely to have been encountered no matter what one has read, whereas many participants may not have encountered lower diversity words simply because the topics and types of written material in which they occur are more restricted.
This repository contains data, materials and code for computing contextual representations and the semantic diversity metric from Cevoli, Watkins & Rastle (2020). See readme file for more detail.
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