A discovery procedure for synlexification patterns in the world’s languages (2025)

Abstract: Synlexification is the pattern of crosslinguistic lexical semantic variation whereby what is expressed in a single word in one language, is expressed in multiple words in another (e.g., French ‘monter’ vs. English ‘go+up’). We introduce a computational method for automatically extracting instances of synlexification from a parallel corpus at a large scale (many languages, many domains). The method involves debiasing the seed language by splitting up synlexifications in the seed language where other languages consistently split them. The method was applied to a massively parallel corpus of 198 Bible translations. We validate it on a broad sample of cases, and demonstrate its potential for typological research.

## introduction languages vary in how they 'package' the same conceptual content in words. variation in colexification -a word in one language having two or more (partial) translation equivalents in another (e.g., english blue translating to russian sinij 'dark blue' and goluboj 'light blue'), has been widely studied @xcite @xcite . another kind of variation occurs when a word in one language is, on the same occasion, translated as two or more words in another language. for example, french monter translates to english go and up. here, the complex concept expressed by a single lexical item in one language is split into two constituent concepts in another language -i.c. english go expressing 'motion', and up the 'vertically elevated' nature of the goal location. while this kind of variation has been studied for individual cases, its generalization was only recently explicated by @xcite , who dubbed the phenomenon 'synlexification', and its inverse 'circumlexification' (e.g., french monter synlexifies what english go+up circumlexifies). parallel corpora have been successfully used to investigate crosslinguistic patterns of colexification @xcite @xcite . however, extant computational approaches are by design unable to find cases of synlexification. furthermore, existing corpus-based studies for individual cases do not allow for general discovery across semantic domains, which would be desirable to better understand the determinants of the typological variation in synlexification patterns. our procedure aims to overcome these challenges. in this paper, we first review corpus-based studies of synlexification across several semantic domains, motivating a more systematic approach. we then introduce a two-step model for automatically extracting synlexification patterns from parallel corpora. we validate the extracted patterns through comparison with documentary resources (grammars and dictionaries), and show that our method captures both many known and novel cases of synlexification. finally, we present an initial exploration of the typological variation. code and (shareable) data are available through https: //github.com/dnrb/synlexification . ## a synlexification detection model to find instances of variation in the lexification of the same concepts, we first have to identify such concepts. given that translations of the same message can be expected to express more or less the same lexical-semantic content, we can use a bitext b t between a seed language s and a target language t to provide us with comparison concepts (i.e., analytic concepts that allow us to compare languages without making a claim as to their languagespecific validity; haspelmath, 2018), and apply word alignment techniques @xcite to determine if there are recurrent many-to-one mappings between multiples of words in s and singleton words in t. this, then allows us to compare across target languages if the same multiples are aligned to singletons across target languages. step 2 of the method describes this procedure. however, a seed language may synlexify what target languages circumlexify, and one-to-many alignments from a seed language to a target language are not comparable: if english enter aligns with dutch ga+binnen and german tritt+ein, we have no way of knowing whether dutch and german circumlexify the complex concept expressed by enter similarly. to maintain the set-up of step 2 (i.e., finding many-to-one s-to-t mappings), step 1 creates a synthesized version of the seed language corpus in which seed language words are artificially circumlexified if other languages reliably do so. ## experimental set-up corpus we test our model on a corpus of bible translations gathered through the bible.is api. while this corpus has issues of ecological validity owing to the nature of the concepts expressed (being exogenous to many cultures) and the frequent production of these texts by non-native speakers @xcite , it has been used extensively in successfully identifying patterns of crosslinguistic lexical semantic variation that align with observations based on other data sources @xcite @xcite . recognizing the nonidentity between the translated, religion-oriented variety of a language and other, more ecologically valid, genres, we use the term doculect @xcite to refer to the variety of a language documented through translation. with these caveats, we treat the results as a lower-bound estimate of the real variation. preprocessing a sample of 198 doculects was derived through diversity sampling @xcite ensuring areal and genetic diversity (see appendix a for a list of doculects). the seed doculect, not part of the sample, was set to be the world english bible translation. the text of this doculect was preprocessed by lemmatizing, pos-tagging, and dependency-parsing it with spacy @xcite and subsequently splitting derivationally complex words (e.g. un-believe-able) through celex2 @xcite , as these reflect complex meanings that may be synlexified in other doculects. target doculects were preprocessed by removing punctuation and segmented with vorm, a state-of-the-art unsupervised canonical morphological segmentation model @xcite , which segments words into stems and affixes. alignments for both s and s ′ were subsequently derived with eflomal @xcite , using the 'growdiag-final-and' heuristic. the alignment in step 1 was done with stems in the target doculects only (as inclusion of affixes at this state led to noise in the procedure), whereas the alignment for step 2 also included affixes. parameters the significance threshold θ f e was set at 1 efoot_0 and the minimum number of languages for which a the ⟨v s , p ⟩ association was significant was set as θ @xmath0. both values were based on posthoc assessment of the extraction quality and more complete parameter tuning on a benchmark set will be left for future research. among the valid circumlexified seed language word pairs, only those that @xmath1. ## validating the model step 1 of the method splits 896 vocabulary types in the seed language, including some cases that are very frequently split among the target doculects, such as @xmath2. notably, most splits involve splitting a word type into the word type itself and an additional element, though cases like @xmath3. a larger sample is presented in appendix b with the full data being available in the repository. based on the circumlexified seed doculect corpus, a total of 2, 563 comparison meaning pairs with a @xmath4, and alignment patterns into the 198 target doculects were extracted with step 2 of the extraction algorithm. while the next section demonstrates what can be done with these data, here we first provide a posthoc validation of the model. ## conclusion this paper introduced a novel method for extracting patterns of synlexification from a parallel corpus at the scale of 198 languages and the full lexicon and validated it on over 40 cases. while the model performed generally well, substantial room for improvement remains. first, replacing seed language words by other seed language words in step 1 means that the (co)lexification pattern of the seed language still affects what alignments are likely to be made. explorations of methods that infer latent discrete n-tuples (e.g., through topic modelling, cf. blei and lafferty, 2009) prove difficult to tune to yield desired results. in future work, we hope to develop such improvements, create more rigorous methods of evaluation, and apply the method to more ecologically valid corpora.