Large-Scale Bitext Corpora Provide New Evidence for Cognitive Representations of Spatial Terms (2024)

Abstract: Recent evidence from cognitive science suggests that there exist two classes of cognitive representations within the spatial terms of a language, one represented geometrically (e.g., above, below) and the other functionally (e.g., on, in). It has been hypothesized that geometric terms are more constrained and are mastered relatively early in language learning, whereas functional terms are less constrained and are mastered over longer time periods (Landau, 2016). One consequence of this hypothesis is that these two classes should exhibit different cross-linguistic variability, which is supported by human elicitation studies. In this work we present to our knowledge the first corpus-based empirical test of this hypothesis. We develop a pipeline for extracting, isolating, and aligning spatial terms in basic locative constructions from parallel text. Using Shannon entropy to measure the variability of spatial term use across eight languages, we find supporting evidence that variability in functional terms differs significantly from that of geometric terms. We also perform latent variable modeling and find support for the division of spatial terms into geometric and functional classes.

## motivation understanding the cognitive structures underpinning spatial terms has been an object of inquiry within the broad tradition of the cognitive sciences, e.g. @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite . one key issue concerns the range of spatial relationships that are in fact encoded in the class of spatial terms across languages. there are two ways of framing this question. scientists who emphasize the universal aspects of spatial language have focused on the idea that non-linguistic spatial representations (which are presumably universal, e.g. ## relation to other work since this work uses computational linguistic techniques in order to provide evidence for a question of cognitive science, it necessarily falls at the intersection of several related sub-disciplines and lines of inquiry. many cognitive scientists have used experimental techniques in which native speakers of various languages are asked to describe pictures portraying different kinds of spatial relationships. the goal of such studies is to elicit a canonical production of a spatial expression in a constrained setting, to allow cross-linguistic comparison @xcite . this method differs from our current work, in which we deliberately attempt to capture variation between and within speakers of a language by observing multiple target-language usage patterns, all parallel to a particular spatial term in the source language. a second body of work investigates the structural properties of systems of spatial terms across many languages, developing models of partitions of semantic types @xcite . by contrast, our work investigates the cross-linguistic correspondences of the tokens of those types within a large-scale parallel text corpus, but without any reference to external representations of spatial arrays. building on the observation that spatial terms typically express a core sense which refers to relations between objects in the physical world, but also secondary meanings referring to temporal and other more abstract relations, a third body of research has attempted to build word-sense disambiguation tools to distinguish between spatial and non-spatial uses of said terms @xcite . such work has required annotating corpora of text for location phrases, necessitating lists of spatial terms and detailed annotation guidelines @xcite . this body of work is similar to ours in that its models learn from usage patterns of spatial terms within a particular language. most closely related to our work is a series of studies by beekhuizen, stevenson, and colleagues (see e.g. @xcite ), which exploits crowdsourced data and parallel text such as the bible to understand the cognitive properties of spatial concepts. in contrast to our work, they focus on the interaction between static/dynamic and support/containment spatial markings. generally, our work falls within the broader tradition of using multilingual text resources to investigate cognitive science questions: besides spatial terms, examples include the study of color terms @xcite , kinship terms @xcite , pain predicates @xcite , indefinite pronouns @xcite , and motion verbs @xcite . ## methods: spatial term equivalence our goal is to extract basic locative constructions (blcs, i.e. the answer to the question where is the object?) from large-scale bilingual corpora in order to measure the variability in usage. for example, how many different terms are used in french for the concept equivalent to spatial term above in english? this requires two things: first, we need to extract blcs containing spatial terms of interest (e.g. the urn is above your fireplace). second, we need to align french terms corresponding to the identified english terms in pairs of blcs that are translations of each other. the first requirement is non-trivial to do automatically because non-spatial and metaphorical usages are prevalent in standard usage: in the case of 'above', e.g. 'above average profits', 'order from above', 'above the rule of law.' we propose a pipeline approach to carry out such measurements, as shown in figure 1 . the goal of the pipeline is, from parallel corpora, to filter blcs -argued to be the clearest contexts for revealing cognitive differences among spatial terms @xcite . the pipeline is optimized to run on a research compute cluster using parallelized cpu operations over large-scale parallel text corpora. the pipeline is applicable to any bitext corpus for any pair of source and target languages. below we describe the components of the pipeline and their interactions. preliminaries: spatial terms -sometimes called topological relation markers (trms) @xcite )-consist of one or more morphemes, lexical items, or combinations of these expressing a spatial relationship between objects. a distinction can be drawn between simple spatial terms (often closed-class adpositions or morphemes) and compound, or phrasal spatial terms, including spatial nominals. 1 given a semantic spatial relation s, we denote each of the k possible types of the expression of s in language l1 as s l1 k , @xmath0, . . . , k. given a pair of parallel sentences containing s l1 k in l1, we can observe the equivalent j realizations in l2 as s l2 jk . by observing a number of such realizations we can count the cooccurrence frequency of s l1 k with individual types of s l2 jk , and thereby measure the cross-linguistic variability. the cross-linguistic variability we seek to measure involves synchronic usage patterns in specific languages, and does not directly address language change. ## experiment setup the proposed method of measuring cross-linguistic variability has been applied to large parallel text corpus of pairs of sentences from english and seven indo-european plus one finno-ugric language: spanish (es), greek (el), german (de), french (fr), dutch (nl), italian (it), farsi (fa), and hungarian (hu). we chose these languages based on two criteria: (1) the availability of large amounts of data in multiple domains and (2) the availability of language informants to perform the manual normalization step in our pipeline. for tokenization, we use the stanford corenlp tokenizer when available (english, spanish, german, and french) and the moses tokenizer otherwise. 2 for the syntax match component of the pipeline, we use stanford corenlp 4.5.1 @xcite . specifically, we use the neural network transition-based dependency parser @xcite trained on english university dependencies. 3 for word alignment, we use giza++ @xcite from the moses lang #sent #blc sources de 25.3m 30,851 b, e, os, gv, q, t el 42.7m 58,581 b, e, os, gv, q, t es 65.4m 70,693 b, e, os, gv, q, t, u fa 7.5m 8,882 tz, os, gv, q, t fr 62.6m 35,229 b, p, i, os, u hu 43.9m 59,579 b, e, os, gv, q, t it 38.2m 50,839 b, e, os, gv, q, t nl 40.0m 55,853 b, e, os, gv, q, t ## discussion and analysis generally, our corpus-based results provide support for the two classes of spatial language shown in the cognitive science literature. we now turn our discussion to the more fine-grained nuances regarding the findings. linking hypothesis to results: our general hypothesis is that there are differences in crosslingual variability between different classes of spatial terms. the results in §5.1 show functional terms have significantly different mean entropy compared to geometric terms. the direction of the difference is consistent with the hypothesis that functional terms should exhibit more cross-lingual variability than geometric ones. the larger variance of the functional class also matches the initial prediction. all results are anchored with the same set of english terms as s l1 k in equation 1. so when we say the english term 'on' has higher cross-lingual variability than 'below', we are only comparing between terms in the same language (english). our results say nothing about the inherent variability of words in, e.g., hungarian vs. german or hungarian vs. english. 6 one assumption is that the choice of languages in l2 should not impact our comparison of l1 term entropies as long as as the set of l2 languages are held constant in our analysis. this is reasonable, but in future work we would like to confirm with a broader set of l2 languages that are either related and unrelated to l1. ## conclusion we find that cross-linguistic variability in spatial term usage is consistent with the hypothesis (landau 2016) of two distinct cognitive representations of spatial terms: one functional (dependent on the force-dynamic interactions between the figure and ground), and the other geometric (defined by the distance and direction of the figure from the ground along primary, secondary, or tertiary axes). the current study adds a new type of evidence for the existence of the two distinct classes to prior studies based on child language development patterns and adult elicitation paradigms. this initial finding would seem to motivate various future investigations. it is desirable to scale up the current analysis and validate it against a larger and more typologically diverse set of languages, requiring automatic morphological parsing to be added to the filtration pipeline. languages using combinations of morphemes to mark spatial relations will require extension of the current shannon entropy measure (equation 1), to allow for joint bigram and trigram probabilities in addition to the unigram probabilities. lastly the data presented here suggest a possible connection between the grammatical categories used by languages to express the two classes of spatial terms, and the theory of formal markedness (jakobson). some languages like english use a single grammatical category (prepositions) to express both putative classes of spatial relations. however other languages such as hungarian use both case markings and postpositions to express spatial relations. our data suggest that hungarian case markings are more closely associated with functional spatial relations, while postpositions are associated with geometric ones. this finding, if extended to other languages with complex nominal and verbal morphological strategies for marking spatial relations, suggest a new way of understanding the range of formal strategies employed by languages to express spatial concepts.