Open Information Extraction
60 papers with code • 13 benchmarks • 13 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
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Datasets
Penn Treebank
New York Times Annotated Corpus
QA-SRL
CaRB
GenericsKB
hasPart KB
Most implemented papers
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.
Relation Extraction : A Survey
cipnlu/InformationExtraction
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In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.
Graphene: A Context-Preserving Open Information Extraction System
In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.
WiRe57 : A Fine-Grained Benchmark for Open Information Extraction
We build a reference for the task of Open Information Extraction, on five documents.
Facts That Matter
This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts.
Span Model for Open Information Extraction on Accurate Corpus
Open information extraction (Open IE) is a challenging task especially due to its brittle data basis.
