activity
20162024
most citedNeural-based Noise Filtering from Word Embeddings

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement

Annerose Eichel, Sabine Schulte im Walde

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 ab…

cs.CL2024

Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds

Annerose Eichel, Tana Deeg, André Blessing +3

We present a comprehensive computational study of the under-investigated phenomenon of personal name compounds (PNCs) in German such as Willkommens-Merkel ('Welcome-Merkel'). Preva…

cs.CL20241 cited

Semantics of Multiword Expressions in Transformer-Based Models: A Survey

Filip Miletić, Sabine Schulte im Walde

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…

cs.CL2023

Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain

Annerose Eichel, Helena Schlipf, Sabine Schulte im Walde

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 st…

cs.CL20171 cited

Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu

Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occu…

cs.CL20163 cited

Neural-based Noise Filtering from Word Embeddings

Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu

Word embeddings have been demonstrated to benefit NLP tasks impressively. Yet, there is room for improvement in the vector representations, because current word embeddings typicall…