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Historical job advertisements provide invaluable insights into the evolution of labor markets and societaldynamics. However, extracting structured information, such as job titles, from these OCRed and unstructuredtexts presents significant challenges. This study evaluates four distinct computational approachesfor job title extraction: a dictionary-based method, a rule-based approach leveraging linguistic patterns,a Named Entity Recognition (NER) model fine-tuned on historical data, and a text generation modeldesigned to rewrite advertisements into structured lists.Our analysis spans multiple versions of the ANNO dataset, including raw OCR, automatically postcorrected,and human-corrected text, as well as an external dataset of German historical job advertisements.Results demonstrate that the NER approach consistently outperforms other methods, showcasingrobustness to OCR errors and variability in text quality. The text generation approach performs well onhigh-quality data but exhibits greater sensitivity to OCR-induced noise. While the rule-based method isless effective overall, it performs relatively well for ambiguous entities. The dictionary-based approach,though limited in precision, remains stable across datasets.This study highlights the impact of text quality on extraction performance and underscores the need foradaptable, generalizable methods. Future work should focus on integrating hybrid approaches, expandingannotated datasets, and improving OCR correction techniques to enhance the extraction of structuredinformation from historical texts. These advancements will enable deeper exploration of labor markettrends and contribute to the broader field of digital humanities.

Paper: jdmdh.episciences.org/15373