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 91–100 of 620 papers

Title	Date	Tasks	Status
An MRC Framework for Semantic Role Labeling	Jan 16, 2022	Computational EfficiencyMachine Reading Comprehension	—Unverified
Better Word Representations with Recursive Neural Networks for Morphology	Aug 1, 2013	Language ModellingNamed Entity Recognition (NER)	—Unverified
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling	Oct 1, 2020	Question AnsweringSemantic Role Labeling	—Unverified
Bilingual English-Czech Valency Lexicon Linked to a Parallel Corpus	Jun 1, 2015	Machine TranslationSemantic Role Labeling	—Unverified
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations	Apr 1, 2017	Bilingual Lexicon InductionClassification	—Unverified
An Unsupervised Model for Instance Level Subcategorization Acquisition	Oct 1, 2014	Machine Translationmodel	—Unverified
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae	Jun 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Bootstrapping Semantic Role Labelers from Parallel Data	Jun 1, 2013	Semantic Role Labeling	—Unverified
Automatic Generation of a Lexical Resource to support Semantic Role Labeling in Portuguese	Jun 1, 2015	Semantic Role Labeling	—Unverified
Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling	Jun 1, 2013	Semantic Role Labeling	—Unverified

Show:10 25 50

← PrevPage 10 of 62Next →

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