Automatisierte Identifikation und Lemmatisierung historischer Berufsbezeichnungen in deutschsprachigen Datenbeständen
Occupational information occurs in many historical sources. For a large number of research areas, not only standardization, but above all classification of these is a central prerequisite for analysis. In this articl...
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| Main Authors: | , |
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| Format: | Article |
| Language: | deu |
| Published: |
Forschungsverbund Marbach Weimar Wolfenbüttel / Verband Digital Humanities im deutschsprachigen Raum e.V.
2022-03-01
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| Series: | Zeitschrift für digitale Geisteswissenschaften |
| Subjects: | |
| Online Access: | https://www.zfdg.de/2022_002 |
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| Summary: | Occupational information occurs in many historical sources. For a large
number of research areas, not only standardization, but above all
classification of these is a central prerequisite for analysis. In this
article, the assignment of spelling variants to already defined generic
names of occupations is referred to as lemmatization or normalisation,
while the assignment of the normalised spelling and to a classification
system is referred to as classification. In order to reduce manual
effort, an algorithm for the automated lemmatization of historical,
German-language occupational data is developed. The best result is
achieved with a supervised machine learning approach. Overall, about 72
percent of the occupational data can be lemmatized, and about 98 percent
of these assignments are correct. |
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| ISSN: | 2510-1358 |