Automatic Semantic Role Labeling in Ancient Greek Using Distributional Semantic Modeling

Alek Keersmaekers

Abstract:

This paper describes a first attempt to automatic semantic role labeling in Ancient Greek, using a supervised machine learning approach.
A Random Forest classifier is trained on a small semantically annotated corpus of Ancient Greek, annotated with a large amount of
linguistic features, including form of the construction, morphology, part-of-speech, lemmas, animacy, syntax and distributional vectors
of Greek words. These vectors turned out to be more important in the model than any other features, likely because they are well suited
to handle a low amount of training examples. Overall labeling accuracy was 0.757, with large differences with respect to the specific
role that was labeled and with respect to text genre. Some ways to further improve these results include expanding the amount of training
examples, improving the quality of the distributional vectors and increasing the consistency of the syntactic annotation.