  In this project, we have investigated the use of advanced machine learning
methods, specifically fine-tuned large language models, for pre-annotating data
for a lexical extension task, namely adding descriptive words (verbs) to an
existing (but incomplete, as of yet) ontology of event types. Several research
questions have been focused on, from the investigation of a possible heuristics
to provide at least hints to annotators which verbs to include and which are
outside the current version of the ontology, to the possible use of the
automatic scores to help the annotators to be more efficient in finding a
threshold for identifying verbs that cannot be assigned to any existing class
and therefore they are to be used as seeds for a new class. We have also
carefully examined the correlation of the automatic scores with the human
annotation. While the correlation turned out to be strong, its influence on the
annotation proper is modest due to its near linearity, even though the mere
fact of such pre-annotation leads to relatively short annotation times.
