Open Information Extraction
43 papers with code • 6 benchmarks • 7 datasets
In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Open Information Extraction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Penn Treebank
QA-SRL
GenericsKB
hasPart KB
OPIEC
TupleInf Open IE Dataset
BenchIE
Most implemented papers
OPIEC: An Open Information Extraction Corpus
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
Relation Schema Induction using Tensor Factorization with Side Information
To the best of our knowledge, this is the first application of tensor factorization for the RSI problem.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques.
MinIE: Minimizing Facts in Open Information Extraction
The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner.
