Cet article présente les solutions utilisées pour le Corpus électronique des textes polonais des 17e et 18e siècles afin d’adapter son jeu de balises grammaticales à l’évolution du système morphologique qui a eu lieu au cours de la période. Les 17e et 18e siècles ont été marqués en effet par la formation d’une nouvelle catégorie grammaticale, appelée « masculine-personality » (Pl. *męskoosobowość*). Cette époque marque une transition de l’état ancien à l’état moderne et se caractérise par une variation significative des (...) terminaisons des formes plurielles N, A et V des noms masculins animés à cette époque. Par conséquent, le jeu de balises utilisé pour annoter le corpus, qui a été créé à l’origine pour le polonais moderne, a dû être légèrement modifié à cet égard. La deuxième partie de l’article décrit les recherches menées à l’aide de ce système d’annotation, qui ont montré que la différenciation des terminaisons flexionnelles des noms masculins personnels et impersonnels en polonais était déjà avancée au 17e siècle et presque achevée à la fin du 18e siècle. (shrink)

The aim of the current research is to investigate the feasibility of identifying offensive language in Lithuanian by utilising the Simplified Offensive Language Taxonomy (SOLT). The key principle behind this taxonomy is its ability to complement existing offensive language ontologies and tagset systems, with the ultimate goal of integrating it into publicly accessible Linguistic Linked Open Data (LLOD) resources. The dataset used in the current study is a publicly available corpus of user-generated comments collected from a Lithuanian portal (Amilevičius (...) et al. 2016). The study identified that offensive language predominantly focuses on collective derogatory language rather than individuals. The most common category of offensive language is related to physical and mental disabilities, followed by ideological offenses, xenophobic and sexist remarks, and less frequent categories like ageism, classism, homophobia, and religious discrimination. These results highlight the diverse range of offensive language online and underscore the need to combat discrimination and promote respectful discourse, particularly concerning marginalised groups. (shrink)

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning (...) methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition. (shrink)