PropBank semantic role labeling with predicted predicates.
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Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.
Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features.
#2 best model for Semantic Role Labeling (predicted predicates) on CoNLL 2005
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.
SOTA for Relation Extraction on WLPC
ASPECT-BASED SENTIMENT ANALYSIS CONSTITUENCY PARSING DEPENDENCY PARSING MULTI-TASK LEARNING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING RELATION EXTRACTION SEMANTIC ROLE LABELING (PREDICTED PREDICATES)