On the Distinctive Co-occurrence Characteristics of Antonymy (2025)

Abstract: Antonymy has long received particular attention in lexical semantics.Previous studies have shown that antonym pairs frequently co-occur in text, across genres and parts of speech, more often than would be expected by chance. However, whether this co-occurrence pattern is distinctive of antonymy remains unclear, due to a lack of comparison with other semantic relations. This work fills the gap by comparing antonymy with three other relations across parts of speech using robust co-occurrence metrics. We find that antonymy is distinctive in three respects: antonym pairs co-occur with high strength, in a preferred linear order, and within short spans. All results are available online.

## introduction among various semantic relations, antonymy has long received particular attention. @xcite proposed the co-occurrence hypothesis, suggesting that adjectival antonymy pairs tend to co-occur within a sentence more often than expected by chance. subsequent studies have provided empirical support for this hypothesis @xcite . this co-occurrence tendency has also been observed in parts of speech (pos) other than adjectives @xcite and across various genres @xcite . prior studies have identified several characteristics of the co-occurrence of antonymy pairs. one is that co-occurrence is likely to be ordered. each antonymy pair often appears in a specific linear order @xcite @xcite . the more frequent antonym tends to precede its less frequent counterpart @xcite . another characteristic is that antonymy pairs likely co-occur in close proximity. this proximity typically falls within five-word windows or appears in coordinated constructions @xcite . therefore, antonymy pairs are characterised by an intra-sentential co-occurrence pattern, which might be distinctive of antonymy. however, this assumption remains largely untested, as the intra-sentential co-occurrence characteristics of non-antonymy relations have been underexplored. some indirect evidence does exist. for example, hypernymy or holonymy pairs can be retrieved using lexico-syntactic patterns @xcite @xcite . yet these approaches rely on specific constructions rather than reflecting the general co-occurrence patterns within sentences. as a result, existing work is insufficient to determine whether the co-occurrence pattern uniquely characterises antonymy. there is also a methodological concern. prior studies commonly relied on raw co-occurrence counts or pointwise mutual information (pmi) of word pairs. such metrics are biased toward extremely low or high frequencies that are common due to the sparsity of word frequencies @xcite . the sparsity also makes many statistical tests (such as the chisquare test used by @xcite ) unreliable @xcite , raising concerns about whether the co-occurrence observed is truly significant. if antonymy indeed stands out in its cooccurrence, this distinctiveness needs to be established rather than assumed. understanding different semantic relations in natural text is foundational not only to linguistic theory but also to computational modeling. recently, pretrained language models (plms) have exhibited a puzzling phenomenon, antonymy bias. regardless of architecture and size, plms perform substantially better on predicting the antonym of a given word over other relations @xcite . establishing the distinctive co-occurrence characteristics of antonymy will lay the groundwork for analysing the semantic behavior of plms trained on a plain corpus. research objective in summary, although prior studies have suggested that antonymy exhibits distinctive co-occurrence characteristics, existing evidence remains insufficient to validate this assumption. the present work evaluates and compares the co-occurrence of antonymy and other semantic relations across pos, using robust and statistically grounded co-occurrence metrics. we aim to establish whether antonymy presents a unique pattern of co-occurrence characteristics. ## data we evaluate four semantic relations: antonymy (ant), synonymy (syn), hyper-hyponymy (hyp), and holo-meronymy (hol). the hyper-hyponymy is defined to hold if either word in a pair is a hypernym of the other; similarly, holo-meronymy holds if either is a holonym of the other. this treatment aligns the definitions of all relations with the symmetric nature of antonymy and synonymy, making all relations comparable. we retrieve lemma pairs of nouns, verbs, adjectives, and adverbs in any of the four relations from wordnet @xcite . a series of filtering steps is applied in order to ensure that all lemma pairs are of the same linguistic unit level and are lexico-semantically interpretable. we exclude: 1) pairs containing multi-word expressions, abbreviations, or named entities; 2) pairs where either word has a frequency of zero or one in wordnet; 3) pairs where multiple semantic relations exist between the two lemmas; 4) verb pairs involving linking verbs, auxiliary verbs, or light verbs; 5) hypernymy pairs with a path length more than two in wordnet hierarchy 2 . these procedures result in 25,115 lemma pairs. we estimate the metrics on the corpus of contemporary american english (coca, @xcite . it is designed to reflect the general usage patterns of the english language across balanced genres. choosing coca brings an additional benefit: it is annotated with both pos tags and lemmas. the pos and lemma annotations allow us to efficiently check semantic relations between two co-occurring lemmas using wordnet. to reduce noise, we exclude sentences shorter than five words, which are often exclamatory or fragmentary. this leaves 17,718,403 sentences for analysis. among the 25,115 lemma pairs retrieved 2 because hypernymy pairs with a path length of two or less are most intuitive to human judgments @xcite . as a control, we additionally randomly sample 10,000 lemma pairs from all lemma pairs that cooccur intra-sententially but are unrelated (unr) in any relation defined in wordnet. the filtering procedures described above are applied to unrelated pairs as well. we sort the two lemmas in each pair by descending frequency. ## results we acknowledge that antonymy tends to have extremely high g 2 scores. for nominal antonymy, there are even two pairs, ("child","parent") and ("man","woman"), whose g 2 scores are greater than 100,000. such extremely high outliers might have inflated the average g 2 scores to 11,144 for nominal antonymy, making it the highest among all pos and relations. pos ant hol hyp syn unr noun .13 .05 .02 .02 .01 97% 96% 93% 90% 33% verb .11 -.03 .02 .02 94% -88% 81% 40% adj .11 --.04 .00 91% --61% 21% adv .29 --.05 .03 95% --87% 29% figure 2 shows the distribution of order scores per relation and pos. for non-antonymy relations, the range of order scores tends to be around 0, confirming that there is no clear co-occurrence order pos ant hol hyp syn unr noun 15 22 26 25 30 verb 18 -28 25 50 adj 14 --17 25 adv 11 --20 27 in short, compared to other relations, antonymy pairs co-occur 1) with greater strength, 2) in the order where the more frequent word tends to precede, and 3) at a shorter distance. our findings align with previous observations on antonyms, and extend them to other relations and pos, revealing the distinctive co-occurrence characteristics of antonymy. ## conclusion this work presents an initial exploration of the intra-sentential co-occurrence of antonymy in comparison to other semantic relations across pos. we find that antonymy is consistently distinctive from all other relations across all metrics used. our findings establish a robust empirical foundation for the distributional nature of antonymy, offering a solid empirical basis for its future analysis.