Clustering verbal Objects: Manual and Automatic Procedures Compared

As highlighted by Pustejovsky (1995, 2002), the semantics of each verb is determined by the totality of its complementation patterns. Arguments play in fact a fundamental role in verb meaning and verbal polysemy, thanks to the sense co-composition principle between verb and argument. For this reason, clustering of lexical items filling the Object slot of a verb is believed to bring to surface relevant information about verbal meaning and the verb-Objects relation. The paper presents the results of an experiment comparing the automatic clustering of direct Objects operated by the agglomerative hierarchical algorithm of the Sketch Engine corpus tool with the manual clustering of direct Objects carried out in the T-PAS resource. Cluster analysis is here used to improve the semantic quality of automatic clusters against expert human intuition and as an investigation tool of phenomena intrinsic to semantic selection of verbs and the construction of verb senses in context

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Sweetening Ontologies Cont'd: Aligning Bottom-Up with Top-Down Ontologies

This paper addresses an issue at the interface between language and ontology. Specifically, we report the results of the alignment we performed between the T-PAS ([1]) and DOLCE categories ([2]), and discuss the distinctions and similarities we observed from a cognitive and application-based perspective. The motivation for our work lies in the different nature of the two resources; while T-PAS is a bottom-up system, in which semantic types are identified by manual clustering the fillers of argument positions of verbs gathered from large corpora, DOLCE is top-down ontology, in which categories are not based on extensive linguistic evidence and are stipulated on formal grounds. The preliminary results of the alignment task reveal that the most general types in T-PAS can be mapped fairly well into DOLCE’s upper level. Two substantial issues remain open, the mapping of the Abstract category and the treatment of systematic polysemy. The experiment also assesses the anthropic character of the bottom-up system compared to the top-down system, and the fine-grained granularity of the first compared to the second. On the other hand, the taxonomy of DOLCE is ontologically more solid than the T-PAS hierarchy. The resulting alignment benefits both sides