The present study examines the results of experiments on the automatic classification of German verbs into five aspectual classes [1]: An experiment within an unsupervised framework based on associations of raters [1] and a couple of experiments within a distributional framework, i.e. in window-based and in a subcategorization-frame-based approach [2]. We compare the predictive power of raters’ associations against two types of verbal cooccurrences: i. pure, unstructured co-occurrences and ii. linguistically motivated, well defined co-occurrences which we denote as informed distributional framework. We observed substantial (unsupervised) and excellent (supervised) agreements with a Gold Standard classification.
CITATION STYLE
Richter, M., Hermes, J., & Neuefeind, C. (2019). Aspectual classifications: Use of raters’ associations and co-occurrences of verbs for aspectual classification in German. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11352 LNAI, pp. 467–491). Springer Verlag. https://doi.org/10.1007/978-3-030-05453-3_22
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