Concreteness vs. Abstractness: A Selectional Preference Perspective (2022)

Abstract: Concrete words refer to concepts that are strongly experienced through human senses (banana, chair, salt, etc.), whereas abstract concepts are less perceptually salient (idea, glory, justice, etc.). A clear definition of abstractness is crucial for the understanding of human cognitive processes and for the development of natural language applications such as figurative language detection. In this study, we investigate selectional preferences as a criterion to distinguish between concrete and abstract concepts and words: we hypothesise that abstract and concrete verbs and nouns differ regarding the semantic classes of their arguments. Our study uses a collection of 5,438 nouns and 1,275 verbs to exploit selectional preferences as a salient characteristic in classifying English abstract vs. concrete words, and in predicting their concreteness scores. We achieve an f1-score of 0.84 for nouns and 0.71 for verbs in classification, and Spearman’s ρ correlation of 0.86 for nouns and 0.59 for verbs.

## introduction concepts can be viewed in accordance with how humans perceive them. those that are easily perceptible with any of the five senses are referred to as concrete concepts, whereas those that cannot be seen, heard, touched, smelled, or tasted as abstract concepts @xcite . examples of concrete concepts are axe, cup, salt, and elephant, whereas examples of abstract concepts are belief, spirituality, and intuition. based on an analysis of noun concepts from the university of south florida dataset @xcite and their occurrence in the british national corpus @xcite , abstract words tend to be much more common in everyday usage @xcite . the distinction between concrete and abstract concepts is quite important in linguistics, psycholinguistics, as well as computational linguistics. furthermore, studies have shown that concreteness measures are useful in a number of applications, such as lexicography @xcite , document comprehensibility @xcite , and figurative language detection @xcite @xcite . theories of cognition contend that concrete and abstract words should co-occur most frequently with concrete words because concrete information connects the actual use of both concrete and abstract words to their mental representation @xcite . however, previous corpus-based empirical studies do not show the same pattern. @xcite , @xcite found that concrete words tend to co-occur with other concrete words, whereas abstract words tend to co-occur with other abstract words. zooming into more specific co-occurrence conditions, frassinelli and schulte im @xcite however demonstrated a more diverse empirical picture: they investigated interaction patterns of abstract and concrete english nouns and verbs in subcategorisation relations, and found that specific combinations indicated specific types of literal vs. figurative language usage, e.g., strongly associated abstract verbs subcategorising concrete direct objects often exhibited metonymy (e.g., recommend a book), while concrete verbs in the same relationship more often indicated literal language use (e.g., write a book). in this study, we focus on selectional preferences as a way to investigate the inconsistencies between cognitive theories and empirical results reported above. selectional preferences indicate the tendency that predicates impose semantic restrictions on the realisations of their complements, i.e., cooccurrence in a syntactic predicate-argument relationship @xcite @xcite . for example, see sentences ( 1 )-(3) with the verb eat, which requires an edible entity as direct object. while the example in (1) is perfectly plausible, the example in (2) is not, because justice violates the selectional preferences of the governing predicate eat. similarly, in (3) we see a violation that can only be resolved as a metaphorical reading. our study investigates whether selectional preferences represent a semantic criterion to establish empirical differences between the two semantic classes of abstract vs. concrete words. we thus suggest a more in-depth look into co-occurrence conditions in comparison to previous work that explored either window-based or purely syntactic co-occurrence. in this vein, we present two data-driven experiments focusing on (i) selectional preferences of english verbs regarding their subcategorisation of subjects and direct objects, and (ii) inverse selectional preferences of english nouns being subcategorised as subjects and direct objects. we use selectional preferences as features (a) in a binary classification task, to distinguish between more abstract vs. more concrete nouns/verbs, and (b) in a regression analysis, to predict the concreteness ratings of nouns and verbs. ## dataset for our research, we utilise the concreteness ratings for approximately 40, 000 english words from @xcite (henceforth, brysbaert norms). the ratings were collected via crowdsourcing on amazon mechanical turk. each word was presented to at least 25 participants who were asked to rate the word on a scale from 1 -5 where 1 indicates clearly abstract and 5 indicates clearly concrete concepts. the scores were then averaged across participants to obtain a mean concreteness rating for each word. the ratings were collected out-of-context and without providing any information about part-of-speech (pos). in a postprocessing step, part-of-speech tags and frequencies were added to the target words, based on the subtlex-us corpus @xcite . following schulte im walde and frassinelli (2022), we extracted and added frequency information based on the english web corpus encow16ax foot_0 @xcite , as well as the most frequent pos tag associated with each target word. in our final dataset, we only included targets where the pos provided in the original collection corresponded to the pos extracted from the encow16ax corpus, the corpus that we use in our experiments. we also removed words for which their predominant pos tag does not represent at least 95% of all pos tags of the target, to reduce ambiguity, and all words with a frequency below 10, 000, to remove infrequent words. after filtering, the resulting collection includes 5, 438 noun targets and 1, 275 verb targets. ## methods and experiments in the following, we present our two experiments exploiting selectional preferences to distinguish between degrees of abstractness. the selectional preference features for our verb and noun targets are induced from the encow16ax corpus mentioned above, which contains 20 billion sentences and is syntactically parsed. we focus on two word-class interactions regarding our verb and noun targets. the student is sleeping. here, the verb sleep takes the noun student as subject. we now discuss how selectional preference features for these two cases were computed and used. ## results and discussion figure 1 shows accuracy scores of the binary classification for nominal subjects (left) and direct objects (right) across our binned ranges of concreteness ratings, i.e. classifications between words in the concreteness ranges 1 -2 vs. 2 -3, 3 -4, and 4 -5. unsurprisingly, accuracy increases with stronger differences between the ratings of the two classes. we also indicate the results for binary classification of the complete sets (red dotted lines, also see accuracies in when using direct object selectional preferences, outperforming both subject selectional preference features and co-occurrences in all conditions, with various difference strengths for feature-based and normalisation variants. between feature-based and normalisation variants we do not observe strong differences. the reported best results relying on direct object selectional preferences are obtained with the following hyper-parameters for verb targets: 200 trees, with a depth of 3 and learning rate of 0.05, and for noun targets: 200 trees, with a depth of 7 and learning rate of 0.05. across binary and regression experiments and experiment settings, the obtained results are better for noun targets than for verb targets, which is in line with our previous work (schulte im @xcite . on the one hand, we hypothesise that this is due to the smaller number of data points and higher data skewness for verbs in comparison to nouns, as depicted in the data split in comparing selectional preference features relying on subjects vs. direct objects, we consistently observe that selectional preferences across direct objects provide more salient features for distinguishing between abstract and concrete nouns and verbs than subjects do. in comparison to previous work, our spearman's ρ correlations for predicted noun ratings (0.865) and direct objects selectional preference features are comparable to @xcite , which shows a spearman's ρ correlation of 0.86 for 9, 241 nouns and 0.78 for the extreme 2, 000 nouns. however, their best-performing models utilise both textual embeddings as well as image embedding. our results are able to achieve similar performance on our 4, 538 nouns with only textual selectional preference features. ## conclusion in this study, we explored the use of selectional preferences as a linguistically more specific semantic criterion than purely sentential co-occurrences, when establishing empirical differences between the two semantic classes of abstract vs. concrete english verbs and nouns. within a set of binary classification experiments varying selectional preference features, normalisations, classifiers, and more or less extreme differences in concreteness scores of the words in the classes, simple co-occurrence generally outperformed the semantically more finegrained selectional preferences; in contrast, selectional preferences for direct objects improved over subject preferences and co-occurrences when used in the more fine-grained concreteness predictions of regression models. so overall, the more fine-grained semantic features are helpful in the more fine-grained perception-based semantic distinctions, and the core information in these combinations are verb-object semantic subcategorisations.