E xp L ay: A new Corpus Resource for the Research on Expertise as an Influential Factor on Language Production (2025)

Abstract: This paper introduces the ExpLay-Pipeline, a novel semi-automated processing tool designed for the analysis of language production data from experts in comparison to the language production of a control group of laypeople. The pipeline combines manual annotation and curation with state-of-the-art machine learning and rule-based methods, following a silver standard approach. It integrates various analysis modules specifically for the syntactic and lexical evaluation of parsed linguistic data. While implemented initially for the creation of the ExpLay-Corpus, it is designed for the processing of linguistic data in general. The paper details the design and implementation of this pipeline.

## introduction this research is grounded in three core assumptions concerning the influence of expertise on cognition and language production. first, it draws on the principle of linguistic relativity @xcite , which postulates that language plays a role in shaping thought, attention allocation, and cognition in general. empirical support for linguistic relativity has been documented across various cognitive domains, includ-ing color perception @xcite , the conceptualization of motion events @xcite and the use of spatial frames of reference @xcite . second, effects similar to linguistic relativity are observed beyond language: expertise, whether professional or personal, can shape cognition in a manner analogous to language. for instance, a neuro-imaging study @xcite found structural alterations in the posterior hippocampus of taxi drivers compared to non-drivers, suggesting that its expansion results from extensive navigational experience. other findings reveal a significant improvement in reaction time for e-sports players @xcite as well as decision making and dexterity @xcite for nonprofessional gamers (semi-experts), compared to laypeople. effects of domain-specific expertise on attention and cognition have also been documented, for example in the field of architecture. in a previous eye-tracking study using stimuli similar to those in the present research, @xcite found that architects and laypeople differ systematically in how they allocate visual attention. while laypeople focused more on human figures in indoor scenes; architects attending to outdoor scenes concentrated longer on architectural elements, particularly upper-level features like roofs, whereas laypeople remained focused on elements at eye level. third, rational communication aims to maintain linguistic code maximally efficient and to this end adapts dynamically to situational and communicative demands. just as language influences cognition, expertise influences language production. this is reflected in domain-specific, conventionalized linguistic codes @xcite , which facilitate both perception and communication within specialized fields. such patterns are evident in domain-related language use and mirror the cogni-tive effects of linguistic relativity discussed earlier. this phenomenon has been observed across various domains, including literary discourse @xcite , the physical sciences @xcite , and diachronic shifts in scientific english @xcite @xcite @xcite as well as scientific german @xcite . domain-specific features also emerge in the use of linguistic structures such as compounding @xcite and metaphor usage @xcite @xcite in scientific and technical texts. despite growing interest in the cognitive effects of expertise, little is known about how architectural expertise influences spatial cognition and its linguistic encoding. this study addresses this gap by analyzing how architects describe urban and natural landscapes. to investigate the linguistic manifestations of expertise in architecture, a dedicated corpus resource was curated and subjected to a preliminary linguistic analysis. as an initial exploratory step, the study focused on syntactic and lexical complexity as indicators of domain-specific language use, comparing the speech production of semi-expert participants (students of architecture) with that of non-expert controls (students of german language and literature). the metrics selected incorporate a range of syntactic and lexical measures, thereby capturing a broad variety of structural linguistic features that may exhibit domain-specific variation across the two groups. given the central role of communicative efficiency, we decided to focus on linguistic complexity as a suitable entry point for exploration of experts' language use. a higher communicative efficiency is often associated with denser, more complex structures (compared to more linear constructions), suggesting the hypothesis that expert language production may exhibit greater structural complexity than that of non-experts. this preliminary analysis primarily serves to demonstrate the capabilities of the parsing and evaluation pipeline presented in this paper. it is not intended as an exhaustive account of architectural expertise in language use. ## previous work most existing studies on complexity measures such as dependency length so far focus on dependency processing @xcite rather than on dependency production. moreover, they tend to treat expertise as a factor either in the processing of other expert's data @xcite or in written expert language such as scientific discourse @xcite . studies applying the universal dependencies (ud) framework @xcite to spoken data usually focus on the creation of spoken language treebanks @xcite rather than addressing differences between the groups of speakers who produced the linguistic data for those treebanks in the first place. while @xcite at least address some particularities of oral data during the annotation process of the resource, in general very little attention is given to the characteristics of the speakers who produced the linguistic material and possible differences among groups (such as experts vs. non-experts). to address the gap between these two areas, the present study curates experimentally elicited spoken data from both expert and nonexpert participants. in doing so, it offers a novel corpus resource to facilitate further investigation into how expertise shapes linguistic structure in spoken language. this approach is motivated in particular by the eye-tracking findings of @xcite , which revealed systematic domain-dependent differences in visual attention patterns between architects and non-architects. these findings suggest domain-specific cognitive processing, and, by extension, the possibility of domain-specific linguistic realizations of such cognitive behaviors, consistent with the study's core assumptions. so we aspire to use a corpus-based and computational linguistic approach to analyze verbalizations in a similar experimental set-up as the one used in the eye-tracking study. to conduct an initial exploratory analysis of potential syntactical and lexical differences between expert and non-expert verbalizations in addition to the curation of the resource itself, this study draws on established (syntactic and lexical) complexity metrics. these include dependency distance @xcite , dependency and constituent-tree tree height @xcite , dependency-based clause count @xcite , and constituency-based phrase count @xcite as well as word class @xcite . additionally, following the methodology of park (2024) we apply principal component analysis (pca) to generate a combined syntactic complex-ity score, using the pc-loadings to determine the weightings of individual metrics contributing to the combined score. ## expplay release the initial release of the explay-resource comprises the raw (unparsed) data, the parsing and evaluation pipeline, as well as the parsed corpus of experimentally elicited spoken language produced by experts and non-experts in the field of architecture. following the silver standard approach described in @xcite , the dataset was manually pre-processed, automatically parsed, and partially curated across multiple linguistic levels using the explay-pipeline introduced in this paper. this pipeline integrates several state-of-theart tools for natural language processing, linguistic annotation, and the evaluation of linguistic structures. the full resource including the pipeline and corpus is made freely available on gitlab. 1 the entire dataset can be accessed under a cc by 4.0 license on osf foot_1 , to support open-access initiatives and facilitate accessible future research in linguistics. ## preliminary analysis of syntactic and lexical complexity in a preliminary evaluation of the newly created corpus, the syntactic and lexical evaluation modules of the explay-pipeline were applied to the rawfile-parse of the corpus. this served two purposes: running a field test on the pipeline and the evaluation modules, as well as providing an initial exploration of the new resource.