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of 45
pro vyhledávání: '"Walde, Sabine Schulte im"'
We present a novel dataset for physical and abstract plausibility of events in English. Based on naturally occurring sentences extracted from Wikipedia, we infiltrate degrees of abstractness, and automatically generate perturbed pseudo-implausible ev
Externí odkaz:
http://arxiv.org/abs/2404.04035
Autor:
Eichel, Annerose, Deeg, Tana, Blessing, André, Belosevic, Milena, Arndt-Lappe, Sabine, Walde, Sabine Schulte im
We present a comprehensive computational study of the under-investigated phenomenon of personal name compounds (PNCs) in German such as Willkommens-Merkel ('Welcome-Merkel'). Prevalent in news, social media, and political discourse, PNCs are hypothes
Externí odkaz:
http://arxiv.org/abs/2404.04031
Multiword expressions (MWEs) are composed of multiple words and exhibit variable degrees of compositionality. As such, their meanings are notoriously difficult to model, and it is unclear to what extent this issue affects transformer architectures. A
Externí odkaz:
http://arxiv.org/abs/2401.15393
Autor:
Schlechtweg, Dominik, Virk, Shafqat Mumtaz, Sander, Pauline, Sköldberg, Emma, Linke, Lukas Theuer, Zhang, Tuo, Tahmasebi, Nina, Kuhn, Jonas, Walde, Sabine Schulte im
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advanc
Externí odkaz:
http://arxiv.org/abs/2311.12664
Humans tend to strongly agree on ratings on a scale for extreme cases (e.g., a CAT is judged as very concrete), but judgements on mid-scale words exhibit more disagreement. Yet, collected rating norms are heavily exploited across disciplines. Our stu
Externí odkaz:
http://arxiv.org/abs/2311.04563
We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a ne
Externí odkaz:
http://arxiv.org/abs/2304.14745
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives? For example, what drives people to say "grasp the meaning" rather than "understand the meaning" within a specific
Externí odkaz:
http://arxiv.org/abs/2205.11113
Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels, and up to date there is no empirical study establishing the extent to
Externí odkaz:
http://arxiv.org/abs/2205.08939
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change
Externí odkaz:
http://arxiv.org/abs/2106.03111
This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym). Most importantly, we demonstrate across datasets for English and
Externí odkaz:
http://arxiv.org/abs/2106.00055