  Co-speech gesture generation on artificial agents has gained attention
recently, mainly when it is based on data-driven models. However, end-to-end
methods often fail to generate co-speech gestures related to semantics with
specific forms, i.e., Symbolic and Deictic gestures. In this work, we identify
which words in a sentence are contextually related to Symbolic and Deictic
gestures. Firstly, we appropriately chose 12 gestures recognized by people from
the Italian culture, which different humanoid robots can reproduce. Then, we
implemented two rule-based algorithms to label sentences with Symbolic and
Deictic gestures. The rules depend on the semantic similarity scores computed
with the RoBerta model between sentences that heuristically represent gestures
and sub-sentences inside an objective sentence that artificial agents have to
pronounce. We also implemented a baseline algorithm that assigns gestures
without computing similarity scores. Finally, to validate the results, we asked
30 persons to label a set of sentences with Deictic and Symbolic gestures
through a Graphical User Interface (GUI), and we compared the labels with the
ones produced by our algorithms. For this scope, we computed Average Precision
(AP) and Intersection Over Union (IOU) scores, and we evaluated the Average
Computational Time (ACT). Our results show that semantic similarity scores are
useful for finding Symbolic and Deictic gestures in utterances.
