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.

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.

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.