On abstraction: decoupling conceptual concreteness and categorical specificity

Conceptual concreteness and categorical specificity are two continuous variables that allow distinguishing, for example, justice (low concreteness) from banana (high concreteness) and furniture (low specificity) from rocking chair (high specificity). The relation between these two variables is unclear, with some scholars suggesting that they might be highly correlated. In this study, we operationalize both variables and conduct a series of analyses on a sample of > 13,000 nouns, to investigate the relationship between them. Concreteness is operationalized by means of concreteness ratings, and specificity is operationalized as the relative position of the words in the WordNet taxonomy, which proxies this variable in the hypernym semantic relation. Findings from our studies show only a moderate correlation between concreteness and specificity. Moreover, the intersection of the two variables generates four groups of words that seem to denote qualitatively different types of concepts, which are, respectively, highly specific and highly concrete (typical concrete concepts denoting individual nouns), highly specific and highly abstract (among them many words denoting human-born creation and concepts within the social reality domains), highly generic and highly concrete (among which many mass nouns, or uncountable nouns), and highly generic and highly abstract (typical abstract concepts which are likely to be loaded with affective information, as suggested by previous literature). These results suggest that future studies should consider concreteness and specificity as two distinct dimensions of the general phenomenon called abstraction

Conceptual Abstractness: from Nouns to Verbs

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.In questo lavoro presentiamo il tema dell’astrattezza come una caratteristica diffusa del linguaggio, e un nodo cruciale nell’elaborazione automatica del linguaggio. In particolare illustriamo un metodo per la stima dell’astrattezza che caratterizza i verbi a partire dalla composizione dei punteggi di astrattezza degli argomenti dei verbi utilizzando la risorsa Abs-COVER

CONcreTEXT @ EVALITA2020: The Concreteness in Context Task

Focus of the CONcreTEXT task is conceptual concreteness: systems were solicited to compute a value expressing to what extent target concepts are concrete (i.e., more or less perceptually salient) within a given context of occurrence. To these ends, we have developed a new dataset which was annotated with concreteness ratings and used as gold standard in the evaluation of systems. Four teams participated in this first edition of the task, with a total of 15 runs submitted.Interestingly, these works extend information on conceptual concreteness available in existing (non contextual) norms derived from human judgments with new knowledge from recently developed neural architectures, in much the same multidisciplinary spirit whereby the CONcreTEXT task was organized

Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the "person" subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.Comment: Accepted to FAT* 202

FinnFN 1.0: The Finnish frame semantic database

The article describes the process of creating a Finnish language FrameNet or FinnFN, based on the original English language FrameNet hosted at the International Computer Science Institute in Berkeley, California. We outline the goals and results relating to the FinnFN project and especially to the creation of the FinnFrame corpus. The main aim of the project was to test the universal applicability of frame semantics by annotating real Finnish using the same frames and annotation conventions as in the original Berkeley FrameNet project. From Finnish newspaper corpora, 40,721 sentences were automatically retrieved and manually annotated as example sentences evoking certain frames. This became the FinnFrame corpus. Applying the Berkeley FrameNet annotation conventions to the Finnish language required some modifications due to Finnish morphology, and a convention for annotating individual morphemes within words was introduced for phenomena such as compounding, comparatives and case endings. Various questions about cultural salience across the two languages arose during the project, but problematic situations occurred only in a few examples, which we also discuss in the article. The article shows that, barring a few minor instances, the universality hypothesis of frames is largely confirmed for languages as different as Finnish and English.Peer reviewe