Conceptual Abstractness: from Nouns to Verbs
Authors: Davide Colla, Enrico Mensa, Aureliano Porporato, Daniele P. Radicioni (Università di Torino, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Investigating lexical access, repEnglish. Investigating lexical access, representation and processing involves dealing with conceptual abstractness: abstract concepts are known to be more quickly and easily delivered in human communications than abstract meanings (Binder et al., 2005). Although these aspects have long been left unexplored, they are relevant: abstract terms are widespread in ordinary language, as they contribute to the realisation of various sorts of ﬁgurative language (metaphors, metonymies, hyperboles, etc.). Abstractness is therefore an issue for computational linguistics, as well. In this paper we illustrate how to characterise verbs with abstractness information. We provide an experimental evaluation of the presented approach on the largest existing corpus annotated with abstraction scores: our results exhibit good correlation with human ratings, and point out some open issues that will be addressed in future work.

Introduction
Surprisingly enough, most of frequently used words (70% of the top 500) seem to be associated to abstract concepts (Recchia and Jones, 2012). Coping with abstractness is thus central to the investigation of lexical access, representation, and processing and, consequently, to build systems dealing with natural language. Information on conceptual abstractness impacts on many diverse NLP areas, such as word sense disambiguation (WSD) (Kwong, 2008), the semantic processing of ﬁgurative uses of language (Turney et al., 2011; Neuman et al., 2013), automatic translation and simpliﬁcation (Zhu et al., 2010), the processing of social tagging information (Benz et al., 2011), and many others, as well. In the WSD task, abstractness has been investigated as a core feature in the ﬁne tuning of WSD algorithms (Kwong, 2007): in particular, experiments have been carried out showing that “words toward the concrete side tend to be better disambiguated that those lying in the mid range, which are in turn better disambiguated than those on the abstract end” (Kwong, 2008). A recent, inspiring, special issue hosted by the Topics in Cognitive Science journal on ‘Abstract Concepts: Structure, Processing, and Modeling’ provides various pointers to tackle abstractness, by posing it as a relevant issue for several disciplines such as psychology, neuroscience, philosophy, general AI and, of course, computational linguistics (Bolognesi and Steen, 2018). As pointed out by the Editors of the special issue, the investigation on abstract concepts is central in the multidisciplinary debate between grounded views of cognition versus modal (or symbolic) views of cognition. In short, cognition might be embodied and grounded in perception and action (Gibbs Jr, 2005): accessing concepts would amount to retrieving and instantiating perceptual and motoric experience. Typically, abstract concepts, that have no direct counterpart in terms of perceptual and motoric experience, are accounted for by such theories with difﬁculty. On the other side, modal approaches to concepts are mostly in the realm of distributional semantic models: in this view, the meaning of rose is “the product of statistical computations from associations between rose and concepts like ﬂower, red, thorny, and love” (Louwerse, 2011).1 While we do not enter this passionate debate, we start by considering that distributional models are of little help in investigating abstractness, with some notable exceptions, such as the interesting links between abstractness and emotional content drawn in (Lenci et al., 2018). In fact, whilst distributional models can be easily used to express similarity and analogy (Turney, 2006), since they are basically built on co-occurrence matrices, they are largely acknowledged to convey vague associations rather than deﬁning a semantically structured space (Lenci, 2018). As illustrated in the following, our approach is different from such mainstream approach, in that the conceptual descriptions used to compute abstractness and contained in the lexical resources COVER (Mensa et al., 2018c) and ABS-COVER (Mensa et al., 2018b)2 are aimed at putting together the lexicographic precision and richness of BabelNet (Navigli and Ponzetto, 2012) and the common-sense knowledge available in ConceptNet (Havasi et al., 2007). One preliminary issue is, of course, how to deﬁne abstractness, since no general consensus has been reached on what should be measured when considering abstractness or, conversely, concreteness (Iliev and Axelrod, 2017). The term ‘abstract’ has two main interpretations: i) what is not perceptually salient, and ii) what is less speciﬁc, and referred to the more general categories contained in the upper levels of a taxonomy/ontology. According to the second view, the concreteness or speciﬁcity —the opposite of abstractness— can be deﬁned as a function of the distance intervening between a concept and a parent of that concept in the top-level of a taxonomy or ontology (Changizi, 2008): the closer to the root, the more abstract. In this setting, existing taxonomies and ontology-like resources can be directly employed, such as WordNet (Miller et al., 1990) or BabelNet (Navigli and Ponzetto, 2012). In this work we single out the ﬁrst aspect, and 1Modal or symbolic views of cognition should not be confused with the symbolic AI, based on high-level representations of problems, as outlined by the pioneering work by Newell and Simon (such as, e.g., in (Newell, 1980)), that was concerned with physical symbol systems 2https://ls.di.unito.it. focus on perceptually salient abstractness; we start from a recent work where we proposed an algorithm to compute abstractness (Mensa et al., 2018a) for concepts contained in COVER (Mensa et al., 2018c; Lieto et al., 2016),3 and we extend that approach in order to characterise also verbs, whose abstractness is presently computed by combining the abstractness of their (nominal) dependents. Different from most literature we treat abstractness as a feature of word meanings (senses), rather than a feature of word forms (terms).

Conclusion
In this paper we have introduced a method to compute verbs abstractness based on the ABSCOVER lexical resource. We reported on the experimentation, and discussed the obtained results, pointing out some issues such as the problem of the sense selection, and the difﬁculty in characterising more concrete concepts. As regards as future work, the simple averaging scheme on dependents’ abstractness scores can be reﬁned in many ways, e.g., by differentiating the contribution of different sorts of dependents, or based on their distribution. Yet, the set of relations that constitute the backbone of ABSCOVER can be further exploited both for computing the abstractness of dependents, and, in the long term, for generating explanations about the obtained abstractness scores, in virtue of the set of relations at the base of the explanatory power of COVER (Colla et al., 2018). Finally, we plan to explore whether and to what extent our lexical resource can be combined with distributional models, in order to pair those strong associative features with the more semantically structured space described by ABS-COVER.