The Groningen Meaning Bank

By Johan Bos. 2013

153 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Continual Few-Shot Learning for Text Classification (2021, background): “Previous NLU projects also beneﬁted from expert annotations (Basile et al., 2012; Bos et al., 2017; Warstadt et al., 2019; Williams et al., 2020).”
ANLIzing the Adversarial Natural Language Inference Dataset (2020, background): “This suggests that one ideally wants expert annotators for difficult annotation tasks like this one (see also Basile et al. 2012; Bos et al. 2017 for other NLU annotation projects that benefit from experts).”
A Primal Dual Formulation For Deep Learning With Constraints (2019, methodology): “Dataset & Baseline Model: We use the publicly available GMB 4 dataset (Bos et al. [2017]) in our experiments.”
Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations (2018, methodology): “Annotations Dataset # pairs Universal Dependencies (Rudinger et al., 2018b) 42K (41,888) MeanTime (Minard et al., 2016) .7K (738)Event Factuality UW (Lee et al., 2015) 5K (5,094) Groningen (Bos et al., 2017a) 260K (261,406) Named Entity Recognition CoNLL (Tjong Kim Sang and De Meulder, 2003) 60K (59”
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it (2022, methodology): “Second, considering that the models seem to struggle to learn the meaning of sentential operators, it may be necessary to provide additional supervision, for example using treebanks annotated with meaning representations, such as the Groningen Meaning Bank (Bos et al., 2017).”
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020, methodology): “Later proposed datasets include the Groningen Meaning Bank (Bos, 2013), the AMR bank (May, 2016), the DeepBank (Flickinger et al.”
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018, methodology): “Phenomena Dataset # pairs Automated Decomp (Rudinger et al., 2018b) 42K (41,888) 3 UW (Lee et al., 2015) 5K (5,094) 3Event Factuality MeanTime (Minard et al., 2016) .7K (738) 3 Groningen (Bos et al., 2017) 260K (261,406) 3Named Entity Recognition CoNLL (Tjong Kim Sang and De Meulder, 2003) 60K (59,9”