Search

The COVID-19 pandemic has affected every aspect of our lives. Our work assesses whether it has also impacted the usage of the Italian language, particularly its lexicon. We create a new corpus of Italian texts taken from Reddit and apply a recent unsupervised usage change detection method on two sub-corpora, one with data from 2019 and one with data from 2020. The focus of our investigation is short-term usage change. The results for the first 10-top candidates and for a selection of candidates among the top-100 are analyzed, to show that usage change has indeed happened.

Abusive language in online social media is a pervasive and harmful phenomenon which calls for automatic computational approaches to be successfully contained. Previous studies have introduced corpora and natural language processing approaches for specific kinds of online abuse, mainly focusing on misogyny and racism. A current underexplored area in this context is religious hate, for which efforts in data and methods to date have been rather scattered. This is exacerbated by different annotation schemes that available datasets use, which inevitably lead to poor repurposing of data in wider contexts. Furthermore, religious hate is very much dependent on country-specific factors, including the presence and visibility of religious minorities, societal issues, historical background, and current political decisions. Motivated by the lack of annotated data specifically tailoring religion and the poor interoperability of current datasets, in this article we propose a fine-grained labeling scheme for religious hate speech detection. Such scheme lies on a wider and highly-interoperable taxonomy of abusive language, and covers the three main monotheistic religions: Judaism, Christianity and Islam. Moreover, we introduce a Twitter dataset in two languages—English and Italian—that has been annotated following the proposed annotation scheme. We experiment with several classification algorithms on the annotated dataset, from traditional machine learning classifiers to recent transformer-based language models, assessing the difficulty of two tasks: abusive language detection and religious hate speech detection. Finally, we investigate the cross-lingual transferability of multilingual models on the tasks, shedding light on the viability of repurposing our dataset for religious hate speech detection on low-resource languages. We release the annotated data and publicly distribute the code for our classification experiments at https://github.com/dhfbk/religious-hate-speech.

A verb argument position can be described by the semantic type that characterizes the words filling that position. We investigate a number of linguistic issues underlying the tagging of an Italian corpus with the semantic types provided by the T-PAS (Typed Predicate-Argument Structure) resource. Our main interest is to evaluate whether our annotation methodology can be employed effectively for the extension of the annotation of the corpus associated with the resource. In order to achieve this goal we compare quantitative data about the tagging and qualitative data derived from the Inter-Annotator Agreement.

Lexical sets contain the words filling an argument slot of a verb, and are in part determined by selectional preferences. The purpose of this paper is to unravel the properties of lexical sets through distributional semantics. We investigate 1) whether lexical set behave as prototypical categories with a centre and a periphery; 2) whether they are polymorphic, i.e. composed by sub-categories; 3) whether the distance between lexical sets of different arguments is explanatory of verb properties. In particular, our case study are lexical sets of causative-inchoative verbs in Italian. Having studied several vector models, we find that 1) based on spatial distance from the centroid, object fillers are scattered uniformly across the category, whereas intransitive subject fillers lie on its edge; 2) a correlation exists between the amount of verb senses and that of clusters discovered automatically, especially for intransitive subjects; 3) the distance between the centroids of object and intransitive subject is correlated with other properties of verbs, such as their cross-lingual tendency to appear in the intransitive pattern rather than transitive one. This paper is noncommittal with respect to the hypothesis that this connection is underpinned by a semantic reason, namely the spontaneity of the event denoted by the verb.

Introduction to CrOCEVIA monographic issue "Dati empirici e risorse lessicali"

It is common practice in computational linguistics to attempt to use selectional constraints and semantic type hierarchies as primary knowledge resources to perform word sense disambiguation (cf. Jurafsky and Martin 2000). The most widely adopted methodology is to start from a given ontology of types (e.g. Wordnet, cf. Miller and Fellbaum 2007) and try to use its implied conceptual categories to specify the combinatorial constraints on lexical items. Semantic Typing information about selectional preferences is then used to guide the induction of senses for both nouns and verbs in texts. Practical results have shown, however, that there are a number of problems with such an approach. For instance, as corpus-driven pattern analysis shows (cf. Hanks et al. 2007), the paradigmatic sets of words that populate specific argument slots within the same verb sense do not map neatly onto conceptual categories, as they often include words belonging to different types. Also, the internal composition of these sets changes from verb to verb, so that no stable generalization seems possible as to which lexemes belong to which semantic type (cf. Hanks and Jezek 2008). In this paper, we claim that these are not accidental facts related to the contingencies of a given ontology, but rather the result of an attempt to map distributional language behaviour onto semantic type systems that are not sufficiently grounded in real corpus data. We report the efforts done within the CPA project (cf. Hanks 2009) to build an ontology which satisfies such requirements and explore its advantages in terms of empirical validity over more speculative ontologies.