arXiv subjects

Alexander M. Esser

Publications and source records attributed to Alexander M. Esser.

4 recordsLinked to original sources

arXiv2026

Scuba Diving Graphs

Scuba divers form small and structured social groups that are well suited for analysis using methods from Computational Social Science (CSS). This paper proposes a conceptual graph-based framework for modeling scuba dives as social networks. Divers are represented as vertices and their interactions as edges within a temporal network. The model focuses on three dimensions of interaction: physical distance, communicative distance, and emergency distance, reflecting spatial positioning, effectiveness of underwater communication, and the ability to assist in critical situations. While the present work is conceptual and primarily descriptive, the proposed graph representation may provide a foundation for future empirical studies aimed at improving diver interaction, coordination, and safety.

cs.SI↗

arXiv2026

Two-Step Occupation Coding

Occupation coding links job titles in free text to occupational taxonomies and is a core task in labor market research. Existing approaches typically address this problem in a single end-to-end step, jointly identifying job titles and assigning occupational codes. This paper presents a novel two-step approach that separates these tasks. In the first step, a domain-specific Named Entity Recognition (NER) model identifies occupational titles in continuous text, even under noise such as OCR errors. In the second step, the extracted job titles are mapped to a taxonomy, enabling the classifier to focus exclusively on this mapping. We demonstrate that this separation improves accuracy, robustness, and interpretability compared to single-step approaches. The method has been developed for German documents but is transferable to other languages. We further introduce a margin-based confidence criterion for occupation coding, replacing common absolute thresholds. To support reproducibility, we publish the source code and evaluation scripts.

cs.CL↗

arXiv2026

Noise-Aware Named Entity Recognition for Historical VET Documents

This paper addresses Named Entity Recognition (NER) in the domain of Vocational Education and Training (VET), focusing on historical, digitized documents that suffer from OCR-induced noise. We propose a robust NER approach leveraging Noise-Aware Training (NAT) with synthetically injected OCR errors, transfer learning, and multi-stage fine-tuning. Three complementary strategies, training on noisy, clean, and artificial data, are systematically compared. Our method is one of the first to recognize multiple entity types in VET documents. It is applied to German documents but transferable to arbitrary languages. Experimental results demonstrate that domain-specific and noise-aware fine-tuning substantially increases robustness and accuracy under noisy conditions. We provide publicly available code for reproducible noise-aware NER in domain-specific contexts.

cs.CL↗

arXiv2021

Flexible Table Recognition and Semantic Interpretation System

Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible and modular table extraction system. We develop two rule-based algorithms that perform the complete table recognition process, including table detection and segmentation, and support the most frequent table formats. Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method. We conduct extensive experiments on the challenging table recognition benchmarks ICDAR 2013 and ICDAR 2019, achieving results competitive with state-of-the-art approaches. Our complete information extraction system exhibited a high F1 score of 0.7380. To support future research on information extraction from documents, we make the resources (ground-truth annotations, evaluation scripts, algorithm parameters) from our table interpretation experiment publicly available.

cs.CV↗