Historical evolution of concrete and abstract language revisited

Abstract

This paper investigates the historical (1850s-2000s) evolution of semantics in the English language using contemporaneous, decade-specific computational estimates of word concreteness. Study 1 describes the computational method of generating time-locked estimates of concreteness based on the Corpus of Historic American English, and makes available the computed scores for 25,000 English words over 15 decades. We also report several tests of reliability and validity, demonstrating that our historical concreteness scores have high levels of both. Study 2 uses concreteness scores to revisit findings of studies that use a static set of contemporary human concreteness norms to examine historical trends of semantic change. Specifically, we observed (contra Hills & Adelman, (Cognition, 143, 87-92 2015)) that distinct word types of the English language become increasingly more concrete over time and (in line with Hills & Adelman, (Cognition, 143, 87-92 2015) & Hills, Adelman & Noguchi, (The Quarterly Journal of Experimental Psychology, 70(8), 1603-1619 2016)) that relatively concrete words tend to be used more often than abstract ones. We discuss both contrastive and corroborative claims in light of recent work on semantic evolution and argue for the use of time-locked computed estimates over static human norms when examining diachronic linguistic phenomena.

Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach

“…The present study proposes a new semantic similarity algorithm based on that of Snefjella et al (2019) for estimating the degree of different LPP dimensions in L2 production and uses the algorithm to address the following three research questions:…”

“…The current study adapted and extended the algorithm and procedure proposed by Snefjella et al (2019) for measuring diachronic change in word concreteness to compute LPP dimension scores for the five EFCAMDAT2 sub-corpora representing L2 learners of different proficiency levels. This section describes how our algorithm was implemented.…”

“…Meanwhile, they noted that their database can be used to track changes in the concreteness of individual words (e.g., semantic bleaching of the word disaster ). While Snefjella et al (2019) used the SentProp algorithm specifically to evaluate diachronic changes of word concreteness in historical sub-corpora, in our view the algorithm can be extended to examine L2 learners’ development over time or differences across different proficiency levels in terms of not only word concreteness but also other LPP dimensions. Specifically, this algorithm allows us to assess changes in the degree of different LPP dimensions with which L2 learners use the same words as their proficiency levels improve, much in the same fashion it allowed Snefjella et al (2019) to evaluate diachronic changes in word concreteness.…”

“…While Snefjella et al (2019) used the SentProp algorithm specifically to evaluate diachronic changes of word concreteness in historical sub-corpora, in our view the algorithm can be extended to examine L2 learners’ development over time or differences across different proficiency levels in terms of not only word concreteness but also other LPP dimensions. Specifically, this algorithm allows us to assess changes in the degree of different LPP dimensions with which L2 learners use the same words as their proficiency levels improve, much in the same fashion it allowed Snefjella et al (2019) to evaluate diachronic changes in word concreteness. The current study explores the possibility of extending the algorithm to the assessment of five LPP dimensions and to the evaluation of differences among sub-corpora representing L2 learners at different proficiency levels.…”

Diachronic semantic change in language influences, and is constrained by, how people use and learn language

“…For instance, Hills and Adelman (2015) used the psycholinguistic property of concreteness (i.e., how easy is it for a given word to refer to a specific object or entity) as an indicator of language learnability on the basis of prior findings that concrete words tend to be acquired earlier in life as compared to abstract words (De Groot & Keijzer, 2000). Hills and Adelman analyzed historical corpora of American English and found that the use of concrete language has become more prevalent over the past two centuries (see also Snefjella et al, 2019). Monaghan (2014) investigated the relation between self-report age of acquisition ratings and the rate of lexical evolution for 200 English words that are fundamental in most language vocabularies.…”