The GUM corpus: creating multilayer resources in the classroom

By Amir Zeldes. Language Resources and Evaluation, 2016

404 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.

TreeAnnotator: Versatile Visual Annotation of Hierarchical Text Relations (2018, positive): “Rather, a multilayer annotation scenario will be implemented, which has already proven to be useful, for instance, in the GUM project (Zeldes, 2017).”
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation (2020, background): “…of text genres, including talks, legal texts and Wikipedia articles (Sanguinetti and Bosco, 2015); the Universal Dependencies version annotation from the GUM corpus (Zeldes, 2017); the English Web Tree-bank (EWT), a gold standard universal dependencies corpus for English (Silveira et al., 2014).”
Evaluating NLP Models via Contrast Sets (2020, methodology): “We use a subset of the English UD treebanks: GUM (Zeldes, 2017), the English portion of LinES (Ahrenberg, 2007), the English portion of ParTUT (Sanguinetti and Bosco, 2015), and the dependency-annotated English Web Treebank (Silveira et al., 2014).”
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020, methodology): “Model We use a biaffine parser following the architecture of Dozat and Manning (2017) with ELMo embeddings (Peters et al., 2018), trained on the combination of the training sets for the treebanks that we drew test examples from (GUM, EWT, LinES, and ParTUT).”