Open Information Extraction
58 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
Rules still work for Open Information Extraction
To train the model, we manually annotated a large-scale Chinese OIE dataset.
Exploiting Duality in Open Information Extraction with Predicate Prompt
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence.
Linking Surface Facts to Large-Scale Knowledge Graphs
Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
CSJianYang/Multilingual-Multimodal-NLP
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Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.
Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation
Aunsiels/GenT
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Structured knowledge bases (KBs) are the backbone of many know\-ledge-intensive applications, and their automated construction has received considerable attention.
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously.
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection
dd1497/oie-td
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We address the problem of negative transfer in TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system.
Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples.
Open Information Extraction via Chunks
Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC.
Syntactically Robust Training on Partially-Observed Data for Open Information Extraction
In this paper, we propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution based on diverse paraphrase generation.
