A Dataset of Latin Etymologies Extracted from Wiktionary

The NIKAW Project: An Infrastructure of Texts, Entities and Language Models to Study the Circulation of Knowledge in the Ancient World

Sophocles’ Antigone as a Knowledge Graph through a Hybrid Collaborative Workflow with Ontology-Guided LLM Extraction

This work targets a KG for Sophocles’ Antigone that supports two coupled uses: structured retrieval, through integrity and competency questions expressed in SPARQL over dramatic structure and interpretive annotations; and interactive exploration, through a lightweight read client that navigates lines across languages, shows scene context, and reports corpus statistics.

Ontology-Based Semantic Normalization of Resumes for Classification

Rather than scaling performance uniformly across the entire evaluation suite, the ontology layer acts as a targeted traceability and semantic refinement filter that contributes information beyond filtered-profile selection alone and produces a metric-dependent change in classifier behaviour at the validation-selected threshold.

Dataset Curation for Kalabari NMT System

The development of Natural Language Processing (NLP) tools for endangered and low resource languages is fundamentally hindered by the scarcity of high-quality parallel data. Data for languages like Kalabari is not only scarce but often noisy, inconsistently digitized, and orthographically unstandardized. While prior work has leveraged religious texts for corpus creation, the specific challenges of extracting and normalizing morphologically rich languages with complex diacritics remain underexplored. This paper addresses this gap by introducing a reproducible, modular curation methodology tailored for such languages. We document a six-step pipeline that transforms raw digital texts—sourced from the Kalabari Bible (FiaFia Biabulu) and instructional literature (Kalabari Lingua)—into a clean, verse aligned, 10,222-pair parallel corpus. We demonstrate that enforcing Normalization Form C (NFC) is critical for preserving sub-dot diacritics, and we validate the corpus by training a baseline Transformer NMT system. Our contributions are threefold: (1) a transferable curation framework for endangered languages, (2) the first sizable Kalabari-English parallel corpus, and (3) baseline experiments that reveal both the promise and the hallucination pitfalls of training on highly constrained, domain-specific data.

SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit

Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing. Diachronic corpus research instead examines interpretable dimensions such as syntactic behaviour, morphology, and constructional patterns, but typically through separate analytical workflows. We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage. SynFlow converts linguistic observations into period-specific distributions and applies a shared workflow across dependency-based co-occurrences, morphological features, constructional configurations, and externally derived representations such as Frame Semantics. It supports different distance measures, together with value-level decomposition, statistical testing, and incremental clustering of lexical fillers. We demonstrate SynFlow through a qualitative case study of the German adjective viral, showing how a single semantic development is reflected across syntactic, lexical, constructional, and morphological dimensions. We further report previously published results on SemEval-2020 Task 1 to situate the performance of these representations relative to existing lexical semantic change detection systems.

ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts

The automatic structural analysis of legal texts is a cornerstone of legal technology, yet the extraction of their logical components remains a significant challenge. In this paper, we introduce the task of identifying and segmenting legal conditions (Tatbestand) and legal consequences (Rechtsfolge) within German statutory texts. To support this task, we present ANNOTARES (Annotations of Tatbestand-Rechtsfolge Sequences), a novel dataset comprising German law texts with span-level annotations. Spanning three distinct legal codes, the dataset is designed to evaluate both domain-specific performance and cross-statute generalizability. We benchmark diverse architectural approaches: a rule-based baseline, CRFs, BiLSTMs, BiLSTM-CRF, and modern Transformer-based models, including BERT variants and LLM-based methods. Our results demonstrate that BERT and LLM-based models achieve superior performance in capturing the complex syntactic structures of legal language. We release our dataset to facilitate further research in automated legal reasoning.