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 331–340 of 620 papers

Title	Date	Tasks	Status
Korean FrameNet Expansion Based on Projection of Japanese FrameNet	Dec 1, 2016	Machine TranslationReading Comprehension	—Unverified
Korean Treebank Transformation for Parser Training	Jul 1, 2012	Semantic Role Labeling	—Unverified
KrakeN: N-ary Facts in Open Information Extraction	Jun 1, 2012	Open Information ExtractionSemantic Role Labeling	—Unverified
K-SRL: Instance-based Learning for Semantic Role Labeling	Dec 1, 2016	Machine TranslationQuestion Answering	—Unverified
UMCC\_DLSI: Textual Similarity based on Lexical-Semantic features	Jun 1, 2013	Machine TranslationNatural Language Inference	—Unverified
Langforia: Language Pipelines for Annotating Large Collections of Documents	Dec 1, 2016	ArticlesDependency Parsing	—Unverified
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets	Jul 1, 2018	Abuse DetectionChunking	—Unverified
Language Processing Infrastructure in the XLike Project	May 1, 2014	Dependency ParsingSemantic Role Labeling	—Unverified
Um reposit\'orio de verbos para a anota \~ao de pap\'eis sem\^anticos dispon\' na web (A Verb Repository for Semantic Role Labeling Available in the Web) [in Portuguese]	Jan 1, 2013	Semantic Role Labeling	—Unverified
Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution	Jul 1, 2012	coreference-resolutionCoreference Resolution	—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