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Relation Extraction
Relation extraction is the task of predicting the attributes of entities and their mutual relationships in a sentence, aiming to identify and classify the relationships between entities in the text. This task is crucial for building relational knowledge graphs and can significantly enhance the performance of natural language processing applications such as structured search, sentiment analysis, question answering systems, and text summarization.
DocRED
DREEAM
TACRED
DeepStruct multi-task w/ finetune
SemEval-2010 Task-8
ACE 2005
Multi-turn QA
CoNLL04
REBEL
Adverse Drug Events (ADE) Corpus
ITER
WebNLG
PFN
ChemProt
SciBert (Finetune)
NYT11-HRL
RERE
ACE 2004
PL-Marker
NYT10-HRL
ReRe
CDR
SAISORE+CR+ET-SciBERT
GDA
FUNSD
Re-TACRED
SpanBERT
NYT
ReDocRED
DREEAM
NYT Corpus
KGPOOL
SemEval 2018 Task 10
SVM with GloVe
NYT21
GAD
BioLinkBERT (large)
NYT-single
ETL-Span
DDI
KeBioLM
NYT29
SciERC
PFN
DWIE
TACRED-Revisited
SKE
ReRe (exact)
Dataset: Relationship extraction for knowledge graph creation from biomedical literature (Gene-Disease relationships)
BioRED
PubMedBERT
WLPC
SpanRel
NYT24
WDec
SemEval-2010 Task 8
LLM-QA4RE (XXLarge)
REBEL
FewRel
ERNIE
Wikipedia-Wikidata relations
ContextAtt
JNLPBA
SciBERT (SciVocab)
2010 i2b2/VA
Spark NLP
LPSC-hasproperty
Stacked_LinkedBERT
ADE Corpus
PFN
sciERC-sent
RELA
Dataset: Relationship extraction for knowledge graph creation from biomedical literature (Gene-Disease relationships) n
MUC6
iDepNN
LPSC-contains
WNUT 2020
Baseline
2018 n2c2 posology
Spark NLP
Google RE
PGR
Spark NLP
DuIE
2012 i2b2 Temporal Relations
Spark NLP