Semantic Role Labeling
118 papers with code • 4 benchmarks • 9 datasets
Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web.
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset.
GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence.