Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations.

Supervised machine learning methods to model word sense often rely on human labelers to provide a single, ground truth label for each word in its context. We examine issues in establishing ground truth word sense labels using a fine-grained sense inventory from WordNet. Our data consist of a sentenc...

Descripción completa

Detalles Bibliográficos

Publicado en:	Language Resources & Evaluation Vol. 46; no. 2; pp. 219 - 253
Autores principales:	Passonneau, Rebecca, Bhardwaj, Vikas, Salleb-Aouissi, Ansaf, Ide, Nancy
Formato:	Artículo
Publicado:	Springer Nature Jun2012
Materias:	Annotations Machine learning Senses Corpora Polysemy Sentences (Grammar) Vocabulary
Acceso en línea:	Ver este registro en EBSCOhost