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
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling	Oct 1, 2022	Semantic Role Labeling	—Unverified
基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention)	Oct 1, 2020	Semantic Role Labeling	—Unverified
Joint A* CCG Parsing and Semantic Role Labelling	Sep 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Joint Arc-factored Parsing of Syntactic and Semantic Dependencies	Jan 1, 2013	ARCDependency Parsing	—Unverified
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis	Jul 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Joint Chinese Word Segmentation, POS Tagging and Parsing	Jul 1, 2012	Chinese Word SegmentationPart-Of-Speech Tagging	—Unverified
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference	Jul 1, 2020	DiagnosticNatural Language Inference	—Unverified
“Kanglish alli names!” Named Entity Recognition for Kannada-English Code-Mixed Social Media Data	Oct 1, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
KeLP: a Kernel-based Learning Platform for Natural Language Processing	Jul 1, 2015	Boundary DetectionFeature Engineering	—Unverified
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings	Jun 1, 2018	ArticlesChunking	—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