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 431–440 of 620 papers

Title	Date	Tasks	Status	Hype
Towards a Better Semantic Role Labeling of Complex Predicates	Jun 1, 2015	Question AnsweringSemantic Role Labeling	—Unverified	0
Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling	Jun 1, 2015	Domain AdaptationMachine Translation	—Unverified	0
Can Selectional Preferences Help Automatic Semantic Role Labeling?	Jun 1, 2015	Semantic Role Labeling	—Unverified	0
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae	Jun 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified	0
Visual Semantic Role Labeling	May 17, 2015	16kAction Classification	CodeCode Available	1
Using Positional Suffix Trees to Perform Agile Tree Kernel Calculation	May 1, 2015	Named Entity Recognition (NER)Relation Extraction	—Unverified	0
Getting the Roles Right: Using FrameNet in NLP	May 1, 2015	Semantic Role Labelingtext annotation	—Unverified	0
Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval	May 1, 2015	Domain Adaptationdomain classification	—Unverified	0
Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles	May 1, 2015	Semantic Role LabelingSentence	—Unverified	0
High-Order Low-Rank Tensors for Semantic Role Labeling	May 1, 2015	Dependency ParsingDimensionality Reduction	—Unverified	0

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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