Open Information Extraction
Open Information Extraction is a task in natural language processing aimed at generating structured, machine-readable information representations from text, typically in the form of triples or n-ary propositions. The goal of this task is to automatically extract entities and their relationships from unstructured natural language texts without predefined relation types or schemas. Open Information Extraction holds significant value in applications such as knowledge graph construction, semantic search, and intelligent question-answering systems.
BenchIE
ClausIE
CaRB
CaRB OIE benchmark (Greek Use-case)
DocOIE-healthcare
DocOIE-transportation
DocIE w transformer
LSOIE
DetIELSOIE
LSOIE-wiki
SMiLe-OIE
NYT
OIE2016
DeepEx (zero-shot)
OpenIE
GEN2OIE (label-rescore)
Penn Treebank
DeepStruct multi-task w/ finetune
Web
DeepEx (zero-shot)
WiRe57
CIGL-OIE + IGL-CA (OpenIE6)