Contextual Variability depends on Categorical Specificity rather than Conceptual Concreteness: A Distributional Investigation on I talian data (2023)

Abstract: A large amount of literature on conceptual abstraction has investigated the differences in contextual distribution (namely “contextual variability”) between abstract and concrete concept words (“joy” vs. “apple”), showing that abstract words tend to be used in a wide variety of linguistic contexts. In contrast, concrete words usually occur in a few very similar contexts. However, these studies do not take into account another process that affects both abstract and concrete concepts alike: “specificity, that is, how inclusive a category is (“ragdoll” vs. “mammal”). We argue that the more a word is specific, the more its usage is tied to specific domains, and therefore its contextual variability is more limited compared to generic words. In this work, we used distributional semantic models to model the interplay between contextual variability measures and i) concreteness, ii) specificity, and iii) the interaction between the two variables. Distributional analyses on 662 Italian nouns showed that contextual variability is mainly explainable in terms of specificity or by the interaction between concreteness and specificity. In particular, the more specific a word is, the more its contexts will be close to it. In contrast, generic words have less related contexts, regardless of whether they are concrete or abstract.

## introduction in the study of lexical semantic representation, an extensive debate focuses on explaining the differences between words referring to concrete and abstract concepts. according to the dual coding theory @xcite , concrete words are represented in two different systems, one language-based and one image-based, while abstract words are primarily or exclusively represented in the former system. the context availability hypothesis @xcite instead argues that all word meanings are represented in a single verbal code, but concrete words have stronger and denser associations to contextual knowledge than abstract ones. both theories agree on two points: i) the meaning of abstract words is essentially acquired via language, for instance, through distributional statistics extracted from the linguistic input, and ii) concrete words are "semantically richer" than abstract ones, thereby explaining their processing advantage, the so-called concreteness effect @xcite . the investigation of the distributional properties of concrete and abstract concepts and words has taken different paths, implementing different metrics to measure how words behave in context (see section 2.1). we hereby use the general term contextual variability @xcite as an 'umbrella' that includes all proposed metrics of contextual behaviors, described in the next section. overall, the previous works on contextual variability showed that words referring to concrete concepts occur in a few but very similar syntagmatic contexts, depending on the fact that their meanings are tied to a fixed class of objects or events in the environment. on the other hand, abstract concepts are characterized by a greater degree of variability across contexts, commonly attributed to their association with less well-defined, intangible experiences or properties. notwithstanding, it is worth noting that prior investigations have mainly focused on the divergence between concrete and abstract concepts, while potentially overlooking any discrepancies in specificity, that is, the level of inclusivity in the referential category. this can be problematic because it may lead to comparisons between very specific concrete concepts like muffler and very generic abstract concepts like manner, or very generic concrete concepts like substance and very specific abstract concepts like sorrow. crucially, generic and specific words may have different contextual distributions: specific words may tend to be used in limited sets of contexts because they denote precise entities occurring in texts characterized by high-resolution semantics. conversely, generic words may be used in a wider range of diverse contexts because they are less precise and, therefore, more easily applicable to different contexts; generic words may occur in texts characterized by low-resolution semantics and, therefore, may occur with a wider range of shallowly-related contexts. with the present study, we tackle the following questions: these questions are addressed through a series of regression studies in which the concreteness ratings @xcite and specificity ratings @xcite of 662 italian nouns are modeled with a set of corpus-based indices representing their context variability. ## experimental investigations given the 662 nouns attested both in itaw2 and itaw10 spaces, we computed all the contextual variability metrics introduced above. we performed the computation with different values of k (5, 10, 20, 50) to see how many contexts/neighbors influence the overall score. subsequently, we ran a series of regression analyses foot_4 aimed at understanding the relations between contextual variability metrics and concreteness/specificity scores. in detail, we ran linear regressions having a context variability metric as the dependent variable; as the independent variable, we consider i) only the concreteness score, ii) only the specificity score, and iii) the interaction between concreteness and specificity. the results of the models are reported in figure 2 . the values in the cells correspond to the coefficient of determination r 2 , which represents the proportion of the total variation in the dependent variable y accounted for by the regression model. values of r 2 closer to 1 (darker colors) imply that the regression model explains a large portion of the variance in context variability. ## discussion and conclusion these analyses provide an enriched view of the relationship between abstraction and contextual variability compared to previous research. in particular, by adding a neglected aspect of abstraction, namely categorical specificity, we observed that the difference in contextual variability is actually more dependent on specificity than on concreteness. these analyses provide an enriched view of the relationship between abstraction and contextual variability compared to previous research. in particular, by adding a neglected aspect of abstraction, namely categorical specificity, we observed that the difference in contextual variability is actually more dependent on specificity than on concreteness. in particular: similar and targeted contexts occur with specific words, while generic words (both abstract and concrete) are associated with more extensive and heterogeneous contexts. to answer our initial research questions, therefore: concreteness explains part of the variation in contextual variability of nouns, but more variation is explained by specificity and by the interaction between the two variables. three key points that the current study makes: first, it revises various terminologies related to contextual variability. second, it is the first study to directly explore contextual variability using the relationship between specificity and concreteness operationalized through human-generated ratings. finally, it is the first study to conduct this analysis within the context of the italian language. the outcomes hereby reported corroborate @xcite 's argument: categorical abstraction (specificity) is a variable that is deeply affected by language rather than by perceptual information, and therefore it has a stronger relationship with how words are used in context (contextual variability). conversely, concreteness is less shaped by the patterns of linguistic occurrences, and arguably it is more deeply affected by perceptual experience. future investigations could focus on fine-grained analyses of different types of nouns, as well as on adjectives and verbs. co-occurrence patterns differ across part-of-speech, but given the limited number of verbs (less than 60), we preferred to focus on nouns only. the present study opens the way to a new line of research in cognitive and computational linguistics and provides a promising different perspective on the analysis of concepts at different levels of abstraction.