Clustering verbal Objects: Manual and Automatic Procedures Compared
Authors: Ilaria Colucci, Elisabetta Jezek, Vít Baisa (Università di Pavia, Lexical Computing Ltd, 2020)
Topic: Lexical and Semantic Resources and Analysis

Abstract
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.

Introduction
Clustering techniques have been used extensively in recent decades in Linguistics and NLP, especially in Word Sense related tasks. As a matter of fact, partitioning data sets on the basis of their similarity at a distributional level clarifies the meaning of lexical elements (Brown et al., 1991). Partitioning verbal arguments, for example, can be beneficial to investigate the sense properties they share but also to explore verbal meaning. In fact, as highlighted by Pustejovsky (1995, 2002), the semantics of each verb is determined by the totality of its complementation patterns and arguments play a fundamental role in verb meaning and verbal polysemy, thanks to the sense co-composition principle. Id est, the process of bilateral semantic selection between the verb and its complement gives rise to a novel sense of the verb in each context of use (ibidem). Clustering lexical items filling the argument positions of a verb is then believed to bring to surface relevant information about verbal meaning and the verb-arguments relation. Clustering them, and especially direct Objects in pro-drop languages such as Italian, allows hence to investigate how to better induce, discriminate and disambiguate verb senses. Because argument fillers share the same semantic nature, they can be grouped and generalized with respect to their content and be associated with semantic types, i.e. empirically identified semantic classes representing selectional properties and preferences of verbs. Clustering of Objects can therefore be used as a survey tool for the intrinsic phenomena of semantic classes and, at the same time, as an object of investigation to improve the clustering automatic models themselves against human partitioning. This paper presents the results of an experiment comparing manual and automatic clustering of Italian Object fillers to be used in verb-sense identification and, along with it, it describes the linguistic phenomena that emerged from the semantic analysis of non-supervised clusters. The comparison concerns the agglomerative hierarchical clustering algorithm of the Sketch Engine corpus tool1 (Kilgarriff et al., 2014) and the manual clustering carried out in the T-PAS resource2 (Ježek at al., 2014), in which verbal senses are identified in context based on the fillers of the argument positions (see section 1.1) and are annotated with a semantic type (ST; see section 1.2) able to identify them. Thanks to their semantic generalization properties, ST are also believed to represent a useful comparative tool between manual and automatic clustering. After presenting the theoretical background of the research, section 2 will cover data, method and work pipeline, while clustering evaluation via metrics and linguistic analysis will be presented in section 3.

Conclusion
The paper presented the statistical and linguistic results of a comparison between SkE unsupervised clustering model and the manual and verb-sense oriented clustering of T-PAS. It highlighted how the noun-oriented model and the verb-oriented one are not overlapping if not partially. The SkE clustering, even if not overlapping, can still be considered as internally compatible with the T-PAS partition, since the homogeneity metric reaches good results. The internal linguistic analysis allowed to identify the semantic quality through the consistency with a semantic type, the internal homogeneity, the adherence with the verb-oriented approach of T-PAS. The reasons that regulate the fragmentation of clusters in SkE, i.e. motivations that follow a fine-grained logic, were then presented. The analysis made possible to shed a light on the semantic compatibility between the two approaches, which seem to pertain to different levels of granularity. The difference in the partition output and the parallel semantic compatibility allows us to claim that the SkE automatic clustering is more useful for the internal investigation of STs than to investigate the verb-Object co-composition relation. It would be interesting to conduct further comparisons between other automatic clustering techniques and that of T-PAS, to investigate additional semantic implications of clustering through noun-based and verb-based approaches.