A Crosslingual Investigation of Conceptualization in 1335 Languages (2023)

Abstract: Languages differ in how they divide up the world into concepts and words; e.g., in contrast to English, Swahili has a single concept for ‘belly’ and ‘womb’. We investigate these differences in conceptualization across 1,335 languages by aligning concepts in a parallel corpus. To this end, we propose Conceptualizer, a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. In a detailed linguistic analysis across all languages for one concept (‘bird’) and an evaluation on gold standard data for 32 Swadesh concepts, we show that Conceptualizer has good alignment accuracy. We demonstrate the potential of research on conceptualization in NLP with two experiments. (1) We define crosslingual stability of a concept as the degree to which it has 1-1 correspondences across languages, and show that concreteness predicts stability. (2) We represent each language by its conceptualization pattern for 83 concepts, and define a similarity measure on these representations. The resulting measure for the conceptual similarity between two languages is complementary to standard genealogical, typological, and surface similarity measures. For four out of six language families, we can assign languages to their correct family based on conceptual similarity with accuracies between 54% and 87%

## introduction languages differ in how they divide up the world into concepts and words. the swahili word 'tumbo' unites the meanings of the english words 'belly' and 'womb'. therefore, english forces its speakers to differentiate between the general body region "front part of the human trunk below the ribs" and one particular organ within it (the womb) whereas swahili does not. similarly, yoruba 'irun' refers to both hair and wool. again, english speakers must ... 'bird' ... while studies have looked at conceptualization within different languages @xcite , we present a crosslingual study that directly compares conceptualization in 1,335 languages. the empirical basis are word and ngram correspondences in the parallel bible corpus (pbc, @xcite ). we introduce conceptualizer, a method that reliably aligns a set of 83 concepts across all pbc languages. the 83 concepts are partly chosen to be well represented in the bible, and partly from swadesh 100 @xcite . the alignments are formalized as a bipartite graph between english (the source) and the target languages. the simple idea underlying conceptualizer-illustrated in figure 1 -is that, starting with one of the 83 concepts in english as the focal concept (the search query), we can identify divergent conceptualizations by first searching for target ngrams highly associated with the focal concept, and then searching for english ngrams highly correlated with the target ngrams we found. if the english ngrams correspond to the original focal concept, then the conceptualizations do not diverge. in contrast, take the example of divergence described above: we start with the focal concept 'hair', find yoruba 'irun' and then two english concepts, not one, that are highly associated with 'irun': 'hair' and 'wool'. this indicates that english and yoruba conceptualizations diverge for 'hair'. our main contribution is that we present the first empirical study of crosslingual conceptualization that grounds the semantics of concepts directly in contexts -the sentences of the parallel corpus. this ensures that our work is based on identical (or at least very similar) meanings across all 1,335 languages we investigate. for example, verse matthew 9:7 has the same meaning in english: "then the man got up and went home.", in chinese: "那個 人就起來，回家去了。" and in each of the other 1,333 languages. such a direct grounding in meaning across a large set of languages has not previously been achieved in work on conceptualization in theoretical or computational linguistics. in addition, we make the following contributions. @xcite we propose conceptualizer, an alignment method specifically designed for concept alignment, that operates on the level of ngrams and ngram sets. (ii) we conduct an evaluation of conceptualizer for the concept 'bird' in all 1,335 languages. the result is a broad characterization of how the conceptualization of bird varies across the languages of the world. out of 1,335 languages, conceptualizer only fails 15 times (due to data sparseness) for 'bird'. (iii) we evaluate conceptualizer for 32 swadesh concepts on a subset of 39 languages for which translation resources exist and demonstrate good performance. (iv) using the ratings provided by @xcite , we give evidence that concreteness (i.e., the degree to which a concept refers to a perceptible entity) causes a concept to be more stable across languages: concrete concepts are more likely to have one-to-one mappings than abstract concepts. @xcite we propose a new measure of language similarity. since we have aligned concepts across languages, we can compute measures of how similar the conceptualization of two languages is. we show that this gives good results and is complementary to genealogical, typological and surface similarity measures that are commonly used. for example, madagascar's plateau malagasy is conceptually similar to geographically distant typological relatives like hawaiian, but also to typologically distant "areal neighbors" like mwani and koti. for four out of six language families, based on conceptual similarity, we can assign languages to their correct family with between 54% and 87% accuracy. ## related work in linguistics, conceptualization has been studied empirically with regards to crosslingual polysemy or colexification @xcite @xcite as well as areal and cultural influences on concept similarity @xcite @xcite . most of this work is based on human annotations, such as clics @xcite @xcite , a database of colexification. however, the coverage of such resources in terms of concepts included, especially for some low-resource languages, is low. therefore we explore the use of an unannotated broad-coverage parallel corpus as an alternative. expanding this work to many languages is important to the extent that we accept some (weak) form of linguistic relativity, i.e., the hypothesis that language structure (including conceptualization) influences cognition and perception @xcite @xcite . methodologically, our work is closely related to @xcite who explores colexification through pbc. he targets specific colexification pairs and investigates their geographical distribution using word alignments. in comparison, our method allows us to identify alignments beyond the word level and therefore richer associations among concepts are obtained. our proposed method conceptualizer is also close to semantic mirrors @xcite , a method to explore semantic relations using translational data. the authors focus on an english-norwegian lemmatized parallel corpus; in contrast, we investigate 1,335 languages, most of which are low-resource and for many of which lemmatization is not available. in addition, this paper is related to recent work that uses pbc to investigate the typology of tense @xcite , train massive multilingual embeddings @xcite , extract multilingual named entities @xcite , find case markers in a multilingual setting @xcite and learn language embeddings containing typological features @xcite . like @xcite ) analyzed the semantic similarity of concepts across languages (mainly european ones). but they use pretrained word embeddings @xcite , which are not available in high enough quality for most of the low-resource languages we cover in this work. computational criteria for language similarity have been taken from typology @xcite @xcite , morphology @xcite and language-model surface similarity @xcite . we propose a new similarity measure, based on conceptualization, with complementary strengths and weaknesses. there is a large body of work on statistical and neural word alignment; recent papers with extensive discussion of this subfield include @xcite @xcite . we show below that the standard alignment method eflomal @xcite does not work well for our problem, i.e., for identifying high-accuracy associations between concepts. ## conclusion & future work we propose conceptualizer, a method that automatically aligns source-language concepts and targetlanguage strings by creating a directed bipartite graph. we investigate the structure of such alignments for 83 focal concepts. our extensive manual evaluation demonstrates good performance of conceptualizer. we introduce the notion of crosslingual stability of a concept and show, using conceptualizer, that concrete concepts are more stable across languages than abstract concepts. we also define conceptual similarity, a new measure of language similarity based on conceptualizer representations. in our experiments, conceptual similarity gives results that partially agree with established measures like typological and areal similarity, but are complementary in that they isolate a single clearly defined dimension of similarity: the degree to which the conceptualization of two languages is similar. in the future, we would like to improve the efficiency of conceptualizer and, extending our work on a sample of 83 in this paper, apply it to all concepts that occur in pbc.