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NLP-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Semantic role labeling

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.

Example:

Housing starts are expected to quicken a bit from August’s pace
B-ARG1 I-ARG1 O O O V B-ARG2 I-ARG2 B-ARG3 I-ARG3 I-ARG3

OntoNotes

Models are typically evaluated on the OntoNotes benchmark based on F1.

Model F1 Paper / Source
He et al., (2018) + ELMO 85.5 Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
(He et al., 2017) + ELMo (Peters et al., 2018) 84.6 Deep contextualized word representations
Tan et al. (2018) 82.7 Deep Semantic Role Labeling with Self-Attention
He et al. (2018) 82.1 Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
He et al. (2017) 81.7 Deep Semantic Role Labeling: What Works and What’s Next

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