SOTAVerified

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

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Showing 351–400 of 620 papers

TitleStatusHype
ImpAr: A Deterministic Algorithm for Implicit Semantic Role Labelling—0
Implicit Semantic Roles in a Multilingual Setting—0
Improved Lexical Acquisition through DPP-based Verb Clustering—0
Improving Chinese-English PropBank Alignment—0
Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames—0
Improving Chinese SRL with Heterogeneous Annotations—0
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments—0
Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language—0
Improving NLP through Marginalization of Hidden Syntactic Structure—0
Improving Statistical Machine Translation with Selectional Preferences—0
Improving the Recall of a Discourse Parser by Constraint-based Postprocessing—0
Improving Unsupervised Question Answering via Summarization-Informed Question Generation—0
Incremental Semantic Role Labeling with Tree Adjoining Grammar—0
Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications—0
Inducing Script Structure from Crowdsourced Event Descriptions via Semi-Supervised Clustering—0
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations—0
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework—0
Inferring Selectional Preferences from Part-Of-Speech N-grams—0
Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles—0
Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches—0
Instrument subjects without Instrument role—0
Integer Linear Programming formulations in Natural Language Processing—0
Integrating Generative Lexicon Event Structures into VerbNet—0
Integrating Multiple Dependency Corpora for Inducing Wide-coverage Japanese CCG Resources—0
Intermediary Semantic Representation through Proposition Structures—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?—0
InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles—0
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles—0
Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis—0
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling—0
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)—0
Joint A* CCG Parsing and Semantic Role Labelling—0
Joint Arc-factored Parsing of Syntactic and Semantic Dependencies—0
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis—0
Joint Chinese Word Segmentation, POS Tagging and Parsing—0
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference—0
“Kanglish alli names!” Named Entity Recognition for Kannada-English Code-Mixed Social Media Data—0
KeLP: a Kernel-based Learning Platform for Natural Language Processing—0
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings—0
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights—0
Knowledge Representation and Extraction at Scale—0
KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents—0
Korean FrameNet Expansion Based on Projection of Japanese FrameNet—0
Korean Treebank Transformation for Parser Training—0
KrakeN: N-ary Facts in Open Information Extraction—0
K-SRL: Instance-based Learning for Semantic Role Labeling—0
Langforia: Language Pipelines for Annotating Large Collections of Documents—0
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets—0
Language Processing Infrastructure in the XLike Project—0
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All datasetsOntoNotesCoNLL 2005CoNLL05 BrownCoNLL05 WSJCoNLL12CoNLL-2009CoNLL-2012

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HeSyFuF188.59—Unverified
2CRF2o + RoBERTaF188.32—Unverified
3MRC-SRLF188.3—Unverified
4ReCAT(pretrained on wikitext103)F188—Unverified
5SRL-MM + XLNetF187.67—Unverified
6CRF2o + BERTF187.66—Unverified
7RoBERTa+RegCCRFF187.51—Unverified
8RoBERTa+CRFF187.27—Unverified
9BiLSTM-Span (Ensemble)F187—Unverified
10BiLSTM-SpanF186.2—Unverified
#ModelMetricClaimedVerifiedStatus
1MRC-SRLF190—Unverified
2SRL-MM + XLNetF189.8—Unverified
3CRF2o + RoBERTaF189.54—Unverified
4HeSyFuF189.04—Unverified
5CRF2o + BERTF189.03—Unverified
6Mohammadshahi and Henderson (2021)F188.93—Unverified
7BiLSTM-Span (Ensemble, predicates given)F188.5—Unverified
8CRF2oF187.87—Unverified
9Li et al. (2019) (Ensemble)F187.7—Unverified
10BiLSTM-SpanF187.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-task w/ finetuneF192.1—Unverified
2DeepStruct multi-taskF192—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF195.5—Unverified
2DeepStruct multi-task w/ finetuneF195.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF197.2—Unverified
2DeepStruct multi-task w/ finetuneF196—Unverified
#ModelMetricClaimedVerifiedStatus
1Ours (High-Order model)F1 (Arg.)90.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HeSyFuAvg. F188.59—Unverified