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 1351–1400 of 1977 papers

TitleStatusHype
HLT-FBK: a Complete Temporal Processing System for QA TempEval—0
Holistic Evaluation of Automatic TimeML Annotators—0
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?—0
How’s Business Going Worldwide ? A Multilingual Annotated Corpus for Business Relation Extraction—0
How Well can We Learn Interpretable Entity Types from Text?—0
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction—0
Identification des probl\`emes d'annotation pour l'extraction de relations (Identification of annotation problem for the relation extraction)—0
Identification of Mentions and Relations between Bacteria and Biotope from PubMed Abstracts—0
Identifying Argumentation Schemes in Genetics Research Articles—0
Identifying High-Level Organizational Elements in Argumentative Discourse—0
Idest: Learning a Distributed Representation for Event Patterns—0
iMETRE: Incorporating Markers of Entity Types for Relation Extraction—0
Impact of Less Skewed Distributions on Efficiency and Effectiveness of Biomedical Relation Extraction—0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective—0
Importance weighting and unsupervised domain adaptation of POS taggers: a negative result—0
Improved Decomposition Strategy for Joint Entity and Relation Extraction—0
Improving Continual Relation Extraction through Prototypical Contrastive Learning—0
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control—0
Improving Discriminative Learning for Zero-Shot Relation Extraction—0
Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference—0
Improving Distantly Supervised Extraction of Drug-Drug and Protein-Protein Interactions—0
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector—0
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding—0
Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings—0
Improving Distantly Supervised Relation Extraction by Natural Language Inference—0
Improving distant supervision using inference learning—0
Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning—0
Improving Knowledge Base Construction from Robust Infobox Extraction—0
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning—0
Improving Neural Relation Extraction with Positive and Unlabeled Learning—0
Improving NLP through Marginalization of Hidden Syntactic Structure—0
Improving Open Relation Extraction via Sentence Re-Structuring—0
Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion—0
Improving Persian Relation Extraction Models by Data Augmentation—0
Improving Relation Classification by Entity Pair Graph—0
Improving Relation Extraction with Knowledge-attention—0
Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification—0
Improving Semantic Parsing via Answer Type Inference—0
Improving Sentence-Level Relation Extraction through Curriculum Learning—0
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures—0
Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models—0
Improving Temporal Relation Extraction with Training Instance Augmentation—0
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource—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
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
Incremental Global Event Extraction—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