Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we tak
e a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge). Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.
The present study was diagnosis and Treatment of dermatophytosis
in Cats. Cases of Mycology Clinic were involved in this study: 58
cats, 1 mounth/4 years aged).Medical exams were: anamnesis,
clinical examination and dermatological exam. Samples we
re
collected from Skin scarping and hair from cats with clinical
suspicion of dermatophytosis. From 58 Cats revaled 18
(31%)showed lesion with dermatophytosis.
