62 citations · 169 across the 17 of their papers we have counts for
3 papers · 2 filters
Exploring the Landscape of Natural Language Processing Research
Tim Schopf, Karim Arabi, Florian Matthes
As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption i…
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge
Tim Schopf, Emanuel Gerber, Malte Ostendorff +1
Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar. Conversely, aspect-based sent…
Efficient Domain Adaptation of Sentence Embeddings Using Adapters
Tim Schopf, Dennis N. Schneider, Florian Matthes
Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore…