On Universal Colexifications (2021)

Abstract: Colexification occurs when two distinct concepts are lexified by the same word. The term covers both polysemy and homonymy. We posit and investigate the hypothesis that no pair of concepts are colexified in every language. We test our hypothesis by analyzing colexification data from BabelNet, Open Multilingual WordNet, and CLICS. The results show that our hypothesis is supported by over 99.9% of colexified concept pairs in these three lexical resources.

## introduction colexification refers to the phenomenon of multiple concepts in the same language being lexified by a single word @xcite . for example, the english word right colexifies the concepts of right (side) and correct (figure 1 ). the term covers both polysemy and homonymy @xcite . in this paper, we posit and investigate the hypothesis that there are no universal colexifications, or more precisely, that no two distinct concepts are colexified in every language. the universal colexification hypothesis is relevant for the task of word sense disambiguation because it would imply that any sense distinction in any language could be disambiguated by translation into some language. it is also related to a famous proposal of @xcite "to restrict a word sense inventory to those distinctions that are typically lexicalized crosslinguistically". if there are no universal colexifications, then a sense inventory based on crosslingual translation pairs would also include all core concepts in existing lexical resources, which would cast doubt on the commonly expressed opinion that wordnet is too fine-grained @xcite . we test our hypothesis by analyzing the colexification data from three different lexical resources: babelnet (bn), open multilingual wordnet (omwn), and the database of cross-linguistic colexifications (clics). taken together, these resources contain over a million lexifications in three thousand languages. the results show that our hypothesis is supported by over 99.9% of colexified concept pairs in these three lexical resources. the structure of the paper is as follows: in section 2, we introduce terminology and background knowledge, formalize the concepts of lexification and colexification, and state our hypothesis. in section 3, we summarize previous research related to colexification. in section 4, we discuss the sources of colexification information which we use to test the hypothesis. section 5 describes how we construct a colexification database from each of these resources. in section 6 we present the empirical verification of the colexification hypothesis and analyze these results further. section 7 concludes the paper. we begin by describing the terminology and background knowledge which contextualizes our work. in particular, we discuss the phenomenon of colexification and how it relates to synonymy, translation, and wordnet. we then provide a formal treatment of these concepts, inspired by the formalization of homonymy and polysemy of @xcite . finally, we formally state and discuss our hypothesis. ## related work approaches to colexification can be divided into three types, which are based on semantic maps, graphs, and databases, respectively. the semantic-map approach to colexification is introduced by @xcite , who focuses on distinguishing senses in the grammatical domain. semantic maps are constructed by cross-linguistic comparison, and contain concepts that have distinct colexifications in at least two different languages. their experiments show that 12 diverse languages are sufficient to build a stable semantic map. our hypothesis relates this statement to entire lexicons of core concepts. @xcite also uses colexification data to build a semantic map for studying the world's lexicons across languages. he observes that the more languages are considered, the more distinctions between senses need to be made. this finding is consistent with our hypothesis, and also raises another open question: is a given pair of colexified concepts colexified universally? the graph-based approach is introduced by @xcite , who analyze cross-linguistic polysemy. they build a weighted colexification graph using data from 195 languages representing 44 language families, and find that clusters of closely-related or similar concepts are often densely connected. @xcite construct colexification graphs in the domain of natural objects to verify if human conceptual structure is universal. analysis reveals universality of similar patterns in semantic structure, even across different language families. the database approach is used by @xcite , who studies colexifications of 100 basic concepts, and introduces heuristics for distinguishing between homonymy and polysemy. georgakopoulos et al. (2020 ## resources in this section, we describe our three resources: babelnet (bn), open multilingual wordnet (omwn), and clics. babelnet @xcite is a multi-wordnet automatically constructed using the expand model based on the princeton word-net. it combines data from wikipedia, wikidata, omegawiki, and various other resources, supplemented by machine translation, to cover nearly 300 distinct languages. each of the multisynsets in bn corresponds to a unique concept, with a unique eight-digit identifier, and an associated part of speech (noun, verb, adjective, or adverb), and contains one or more words which can express that concept in various languages. for instance, the nominal concept tree is represented by synset bn:00078131n which includes the english words tree and arbor, as well as french arbre and italian albero. we use babelnet version 4.0. open multilingual wordnet @xcite one or more languages which lexify a particular concept. for example, sign and mark (english), and signe, témoignage, preuve, and point (french) all share a multi-synset. omwn is based on a set of 5000 core concepts, constructed by @xcite 1 . this list was updated to wordnet 3.0 by the creators of omwn 2 . every wordnet 3.0 synset in this list corresponds to exactly one multi-synset in omwn, and exactly one multi-synset in bn. indeed, both resources are created by applying the expand model to wordnet 3.0. for the purposes of our work, we limit omwn and bn to their respective 5000 synsets corresponding to these core concepts. the database of cross-linguistic colexifications (clics) @xcite ) is an online lexical database containing information on cross-linguistic colexification patterns across thousands of languages from hundreds of language families. clics does not follow any wordnet model, but instead integrates word lists representing thousands of languages, which vary greatly in terms of lexicon coverage. colexification patterns are represented in the form of a network, where the weights express the number of languages that colexify the concept pair. we obtained the data following the procedure of @xcite , which directly facilitates access to colexification data for any concept pair. clics also contains information on the family each language belongs to. ## method for each of the resources described above, we use the following procedure to create a database of concept pairs and colexification information. the first step is to extract from each resource the set of concepts it contains, and the set of words lexifying each concept. for clics, this is relatively straightforward, as the resource is already structured as a database of concepts and lexifications for each language. we access omwn through nltk foot_0 , and bn via its java api foot_1 . each concept in these resources is represented by a multi-synset, which can be extracted using the aforementioned apis. the second step is to map each of the three sets of concepts to each other, so that identical concepts in distinct resources can be associated with one another for our analysis. this is done by using wordnet 3.0 as a pivot. as described in section 4, each of the 5000 core concepts in bn and omwn is already linked to a wordnet 3.0 synset. however, mapping clics to wordnet is not trivial because, unlike bn and omwn multi-synsets, clics concepts have no intrinsic connection to wordnet synsets. therefore, we use a concepticon mapping created by @xcite which links a subset of clics concepts to wordnet. unfortunately, the mapping is incomplete, covering only 1368 (46.9%) of clics concepts. the third step is to enumerate all pairs of distinct concepts. there are approximately 4.3 million possible concept pairs in clics, and 12.5 million possible concept pairs in bn and omwn. although there are millions of concept pairs in each resource, only a subset are lexified by some language (i.e. there exists a language with at least resource languages concepts lexifications colexifications exceptions support clics 3050 2919 1,377,282 75,089 64 99.9% bn 284 5000 1,441,990 88,907 3 99.9% omwn 29 5000 267,503 54,615 4 99. 9% our hypothesis states that the colexification ratio for any concept pair, for any of our databases, is always less than 1, given that it is defined. that is, there is always some language that lexifies both concepts, but does not colexify them. ## results in this section, we describe the empirical validation of our hypothesis on the colexification data from clics, bn, and omwn. our results are summarized in the three most frequently colexified concept pairs in each resource are shown in ## conclusion we have proposed a novel hypothesis which states that there are no universal colexifications. we provided evidence that the few apparent exceptions to the hypothesis that we found in three multilingual resources are attributable to omission errors in the resources. in the future, we plan to leverage our hypothesis to improve the accuracy of multilingual word sense disambiguation. the validation of our hypothesis provides novel insights into several open issues in lexical semantics. it implies that every sense distinction in every language can be disambiguated by translation into some language. it also provides support for the informal conjecture of @xcite that every possible sense distinction can be identified by translation into multiple languages. finally, it furnishes evidence that the fine-granularity of wordnets and multi-wordnets is necessary for distinguishing between lexical translations of concepts.