SOTAVerified

Relation Extraction

Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

Source: Deep Residual Learning for Weakly-Supervised Relation Extraction

Papers

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Showing 1051–1100 of 1977 papers

TitleStatusHype
TEES 2.1: Automated Annotation Scheme Learning in the BioNLP 2013 Shared Task—0
Automatic Extraction of Time Expressions Accross Domains in French Narratives—0
Incorporating Consistency Verification into Neural Data-to-Document Generation—0
Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision—0
Incorporating medical knowledge in BERT for clinical relation extraction—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns—0
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
Incremental Global Event Extraction—0
Incremental Joint Extraction of Entity Mentions and Relations—0
Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers—0
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods—0
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks—0
Information Extraction: An application to the domain of hyper-local financial data on developing countries—0
Information Extraction from Biomedical Texts: Learning Models with Limited Supervision—0
Tel(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery—0
Information Extraction from German Patient Records via Hybrid Parsing and Relation Extraction Strategies—0
TINKER: A framework for Open source Cyberthreat Intelligence—0
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey—0
Infusing clinical knowledge into tokenisers for language models—0
Infusion of Labeled Data into Distant Supervision for Relation Extraction—0
Automatic Evaluation of Relation Extraction Systems on Large-scale—0
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning—0
Pareto Optimal Learning for Estimating Large Language Model Errors—0
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction—0
Integrating Deep Learning with Logic Fusion for Information Extraction—0
Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction—0
Temporal information extraction from clinical text—0
Temporally Anchored Relation Extraction—0
Integrating Word Embedding Offsets into the Espresso System for Part-Whole Relation Extraction—0
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature—0
Interactive Relation Extraction in Main Memory Database Systems—0
Interfacing Sentential and Discourse TAG-based Grammars—0
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints—0
Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder—0
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts—0
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network—0
Temporal Reasoning Graph for Activity Recognition—0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Investigating LSTMs for Joint Extraction of Opinion Entities and Relations—0
Investigating the Challenges of Temporal Relation Extraction from Clinical Text—0
Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism—0
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction—0
Invited Talk: The Case for Universal Dependencies—0
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification—0
Is artificial data useful for biomedical Natural Language Processing algorithms?—0
"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision—0
Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons—0
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach—0
Automatic Animacy Classification—0
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All datasetsDocREDTACREDSemEval-2010 Task-8ACE 2005CoNLL04Adverse Drug Events (ADE) CorpusWebNLGChemProtACE 2004NYT11-HRLCDRGDA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DREEAMF167.53—Unverified
2KD-Rb-lF167.28—Unverified
3SSAN-RoBERTa-large+AdaptationF165.92—Unverified
4SAIS-RoBERTa-largeF165.11—Unverified
5Eider-RoBERTa-largeF164.79—Unverified
6DocuNet-RoBERTa-largeF164.55—Unverified
7CGM2IR-RoBERTalargeF163.89—Unverified
8SETE-Roberta-largeF163.74—Unverified
9ATLOP-RoBERTa-largeF163.4—Unverified
10DRE-MIR-BERTbaseF163.15—Unverified
#ModelMetricClaimedVerifiedStatus
1RAG4REF186.6—Unverified
2DeepStruct multi-task w/ finetuneF176.8—Unverified
3UNiST (LARGE)F175.5—Unverified
4RE-MCF175.4—Unverified
5GenPT (T5)F175.3—Unverified
6RECENT+SpanBERTF175.2—Unverified
7SuRE (PEGASUS-large)F175.1—Unverified
8EXOBRAINF175—Unverified
9Relation ReductionF174.8—Unverified
10RoBERTa-large-typed-markerF174.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SPF191.9—Unverified
2RIFREF191.3—Unverified
3REDNF191—Unverified
4SPOTF190.6—Unverified
5KLGF190.5—Unverified
6RELAF190.4—Unverified
7Skeleton-Aware BERTF190.36—Unverified
8KnowPromptF190.3—Unverified
9LUKEF190.3—Unverified
10EPGNNF190.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Span-levelNER Micro F185.98—Unverified
2Dual Pointer Network(multi-head)Relation classification F180.8—Unverified
3Dual Pointer NetworkRelation classification F180.5—Unverified
4PL-MarkerRE Micro F173—Unverified
5ASP+T5-3BRE Micro F172.7—Unverified
6GoLLIERE Micro F170.1—Unverified
7Ours: cross-sentence ALBRE Micro F169.4—Unverified
8MGERE+ Micro F168.2—Unverified
9HySPA (ours) w/ RoBERTaRelation F168.2—Unverified
10RNN+CNNRelation classification F167.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ReLiK-LargeRE+ Micro F178.1—Unverified
2REBELRE+ Macro F1 76.65—Unverified
3ASP+T0-3BRE+ Micro F176.3—Unverified
4Table-SequenceRE+ Macro F1 75.4—Unverified
5SpERTRE+ Macro F1 72.87—Unverified
6DeeperRE+ Macro F1 72.63—Unverified
7TANLRE+ Micro F172.6—Unverified
8TablERTRE+ Micro F172.6—Unverified
9TriMFRE+ Micro F172.35—Unverified
10Multi-turn QARE+ Micro F168.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PFN (ALBERT XXL, average aggregation)RE+ Macro F183.9—Unverified
2DeeperRE+ Macro F183.74—Unverified
3PFN (ALBERT XXL, no aggregation)RE+ Macro F183.2—Unverified
4SpERT.PL (without overlap and BioBERT)RE+ Macro F182.39—Unverified
5REBEL (including overlapping entities)RE+ Macro F182.2—Unverified
6SpERT.PL (with overlap and BioBERT)RE+ Macro F182.03—Unverified
7CMANRE+ Macro F181.14—Unverified
8Table-SequenceRE+ Macro F180.1—Unverified
9CLDR + CLNERRE+ Macro F179.97—Unverified
10SpERT (without overlap)RE+ Macro F179.24—Unverified
#ModelMetricClaimedVerifiedStatus
1UniRelF194.7—Unverified
2PFNF193.6—Unverified
3SPNF193.4—Unverified
4TDEERF193.1—Unverified
5RIFREF192.6—Unverified
6TPLinkerF191.9—Unverified
7HBT (CasRel)F191.8—Unverified
8RIN (BERT, K=2)F190.1—Unverified
9CGT(UniLM)F183.4—Unverified