We present CoBra, a resource containing triconstituent nominal compounds in English and German. This addresses an understudied aspect of compound processing, since research and resources in psycholinguistics and NLP have mostly focused on two-constituent compounds. In addition, our resource covers both general and scientific language, allowing for a register-informed perspective on compounds. It provides syntactic and semantic annotation of compound structure, in particular of the branching direction (i.e. the internal embedding structure, the Compound Branching) and the semantic relationship between constituents. Annotations are implemented using extensions of Universal Dependencies (UD) labels. To explore applications of our new resource, we also conduct a pilot study investigating the relationship between semantic transparency and branching direction. Our results indicate that there is indeed a correlation. Overall, our resource contributes to gaining a more detailed understanding of the structure and processing of morphologically complex words within the UD framework.

This study investigates how domain expertise shapes spontaneous oral language production, with a focus on architecture. Building on the ExpLay Corpus, which contains image descriptions by speakers with and without architectural training, we analyze linguistic variation by combining Profiling-UD and the DECAF framework. We extract a broad range of syntactic and morpho-syntactic features to build linguistic profiles for both groups and train classifiers to distinguish expert from non-expert productions. Two traditional machine learning models (logistic regression and SVM) are compared with a lightweight BiLSTM and two large language models (GliClass and LLaMA 2). While the expert and non-expert corpora diverge only subtly (pairwise Jensen–Shannon divergence (JSD)= 0.25), the BiLSTM using fastText embeddings achieves the highest F1-score (0.88), outperforming both traditional models and LLMs. This indicates that semantic representations are more predictive of domain variation than purely structural features and that smaller neural architectures generalize better on limited data. Overall, the findings provide empirical evidence that architectural expertise leaves measurable linguistic traces in spontaneous speech, supporting the Grammar of Space hypothesis.

Consistent alignment of multi-modal experimental data—such as verbal utterances in elicitation tasks, (static) visual stimuli, and gaze data—presents a challenge in linguistic research. These elicitations often encode information about the visual perception strategies or cognitive processing of the scene. Thus, it is helpful to transform them into a structured, visually grounded format which captures the visual nature of the data, ideally able to be aligned with the corresponding gaze data. To achieve this, the present paper conceptually proposes the annotation framework for verbal elicitation paths as a data type and presents the first release of the associated newly developed CoordiMap annotation tool. The tool enables structured mapping of verbal elicitation data from experimental studies onto the corresponding visual stimuli. Independent of specific paradigms, the tool supports the annotation of verbal utterances in a linearized form based on coordinates directly marked on the image of the stimulus. The format is conceptually inspired by eye-tracking data formats, in which gaze behavior is represented as temporally linearized paths overlaid on the stimulus. The paper motivates the development of the tool and its annotation methodology by theoretical and experimental considerations regarding the relationship between visual perception and language production. As this a work in progress, the functionality of the annotation tool is demonstrated through an exemplary use case.

Compounding—the creation of highly complex lexical items through the combination of existing lexemes—can be considered one of the most efficient communication phenomenons, though the automatic processing of compound structures—especially of multi-constituent compounds—poses significant challenges for natural language processing. Existing tools like compound-split (Tuggener, 2016) perform well on compound head detection but are limited in handling long compounds and distinguishing compounds from non-compounds. This paper introduces NoCs (non-compound-stable splitter), a novel Python-based tool that extends the functionality of compound-split by incorporating recursive splitting, non-compound detection, and integration with state-of-the-art linguistic resources. NoCs employs a custom stack-and-buffer mechanism to traverse and decompose compounds robustly, even in cases involving multiple constituents. A large-scale evaluation using adapted GermaNet data shows that NoCs substantially outperforms compound-split in both non-compound identification and the recursive splitting of three- to five-constituent compounds, demonstrating its utility as a reliable resource for compound analysis in German.

We present an annotation experiment for the annotation of information status in German TEDx Talks with the main goal to reduce annotation costs in terms of time and personnel. We aim for maximizing efficiency while keeping annotation quality constant by testing various different annotation scenarios for an optimal ratio of annotation expenses to resulting quality of the annotations. We choose the RefLex scheme of Riester and Baumann (2017) as a basis for our annotations, refine their annotation guidelines for a more generalizable tagset and conduct the experiment on German Tedx talks, applying different constellations of annotators, curators and correctors to test for an optimal annotation scenario. Our results show that we can achieve equally good and possibly even better results with significantly less effort, by using correctors instead of additional annotators.

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.