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 |

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 381–390 of 620 papers

Title	Date	Tasks	Status
Right-truncatable Neural Word Embeddings	Jun 1, 2016	Dependency ParsingMachine Translation	—Unverified
Neural Semantic Role Labeling with Dependency Path Embeddings	May 24, 2016	Semantic Role Labeling	CodeCode Available
Persian Proposition Bank	May 1, 2016	Semantic Role Labeling	—Unverified
MEANTIME, the NewsReader Multilingual Event and Time Corpus	May 1, 2016	ArticlesSemantic Role Labeling	—Unverified
Towards Building Semantic Role Labeler for Indian Languages	May 1, 2016	Semantic Role LabelingSentence	—Unverified
Semi-automatically Alignment of Predicates between Speech and OntoNotes data	May 1, 2016	Semantic Role LabelingSentence	—Unverified
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources	Jan 1, 2016	General ClassificationMachine Translation	—Unverified
Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications	Dec 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Extra \~ao de Alvos em Coment\'arios de Not\' em Portugu\^es baseada na Teoria da Centraliza \~ao (Target Extraction in News Reviews in Portuguese based on Centering Theory)	Nov 1, 2015	Semantic Role Labeling	—Unverified
VerbLexPor: um recurso l\'exico com anota \~ao de pap\'eis sem\^anticos para o portugu\^es (VerbLexPor: a lexical resource annotated with semantic roles for Portuguese)	Nov 1, 2015	Semantic Role Labeling	—Unverified

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All datasets OntoNotes CoNLL 2005 CoNLL05 Brown CoNLL05 WSJ CoNLL12 CoNLL-2009 CoNLL-2012

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HeSyFu	F1	88.59	—	Unverified
2	CRF2o + RoBERTa	F1	88.32	—	Unverified
3	MRC-SRL	F1	88.3	—	Unverified
4	ReCAT(pretrained on wikitext103)	F1	88	—	Unverified
5	SRL-MM + XLNet	F1	87.67	—	Unverified
6	CRF2o + BERT	F1	87.66	—	Unverified
7	RoBERTa+RegCCRF	F1	87.51	—	Unverified
8	RoBERTa+CRF	F1	87.27	—	Unverified
9	BiLSTM-Span (Ensemble)	F1	87	—	Unverified
10	BiLSTM-Span	F1	86.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MRC-SRL	F1	90	—	Unverified
2	SRL-MM + XLNet	F1	89.8	—	Unverified
3	CRF2o + RoBERTa	F1	89.54	—	Unverified
4	HeSyFu	F1	89.04	—	Unverified
5	CRF2o + BERT	F1	89.03	—	Unverified
6	Mohammadshahi and Henderson (2021)	F1	88.93	—	Unverified
7	BiLSTM-Span (Ensemble, predicates given)	F1	88.5	—	Unverified
8	CRF2o	F1	87.87	—	Unverified
9	Li et al. (2019) (Ensemble)	F1	87.7	—	Unverified
10	BiLSTM-Span	F1	87.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task w/ finetune	F1	92.1	—	Unverified
2	DeepStruct multi-task	F1	92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task	F1	95.5	—	Unverified
2	DeepStruct multi-task w/ finetune	F1	95.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task	F1	97.2	—	Unverified
2	DeepStruct multi-task w/ finetune	F1	96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ours (High-Order model)	F1 (Arg.)	90.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HeSyFu	Avg. F1	88.59	—	Unverified