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
Latest papers
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
cgraywang/deepex
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EMNLP 2021
We cast a suite of information extraction tasks into a text-to-triple translation framework.
Context-NER : Contextual Phrase Generation at Scale
In this paper, we introduce CONTEXT-NER, a task that aims to generate the relevant context for entities in a sentence, where the context is a phrase describing the entity but not necessarily present in the sentence.
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark
Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner.
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation
In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.
DocOIE: A Document-level Context-Aware Dataset for OpenIE
daviddongkc/DocOIE
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Findings (ACL) 2021
Both DocOIE dataset and DocIE model are released for public.
PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation
In this paper we present our submission for the EACL 2021 SRW; a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction, showcasing it on the Greek language.
Syntactic and Semantic-driven Learning for Open Information Extraction
TangJiaLong/SSD-OpenIE
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Findings of the Association for Computational Linguistics 2020
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction
We introduce a new dataset by converting the QA-SRL 2. 0 dataset to a large-scale OIE dataset (LSOIE).
Disentangling semantics in language through VAEs and a certain architectural choice
ghazi-f/Disentanglement_Transformer
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We present an unsupervised method to obtain disentangled representations of sentences that single out semantic content.
Multi\^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
youngbin-ro/Multi2OIE
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Findings of the Association for Computational Linguistics 2020
In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention.
