Automatic Phrase Recognition in Historical German
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), KONVENS 2021 Organizers, pp. 127–136, Düsseldorf, Germany, 2021.
Due to a lack of annotated data, theories of historical syntax are often based on very small, manually compiled data sets. To enable the empirical evaluation of existing hypotheses, the present study explores the automatic recognition of phrases in historical German. Using modern and historical treebanks, training data for a neural sequence labeling tool and a probabilistic parser is created, and both methods are compared on a variety of data sets. The evaluation shows that the unlexicalized parser outperforms the sequence labeling approach, achieving F1-scores of 87%–91% on modern German and between 73% and 85% on different historical corpora. An error analysis indicates that accuracy decreases especially for longer phrases, but most of the errors concern incorrect phrase boundaries, suggesting further potential for improvement.