Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food•Event nouns for 5 languages.

This paper describes the results of an empirical study on attitude verbs and propositional attitude reports in Italian. Within the framework of a project aiming at acquiring argument structures for Italian verbs from corpora, we carried out a systematic annotation that aims at individuating which verbs are actually attitude verbs in Italian. The result is a list of 179 argument structures based on corpus-derived pattern of use for 126 verbs that behave as attitude verbs. The distribution of these verbs in the corpus suggests that not only the canonical that-clauses, i.e. subordinates introduced by the complementizerte che, but also direct speech, infinitives introduced by the complementizer di, and some nominals are good candidates to express propositional contents in propositional attitude reports. The annotation also enlightens some issues between semantics and ontology, concerning the relation between events and propositions.