Open Information Extraction
67 papers with code • 13 benchmarks • 14 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
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.
Multi-View Clustering for Open Knowledge Base Canonicalization
yang233666/cmvc
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In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
universal-ner/universal-ner
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Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna.
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.
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.
