SOTAVerified

Open Information Extraction

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).

Papers

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Showing 1–10 of 207 papers

TitleStatusHype
ChatPD: An LLM-driven Paper-Dataset Networking SystemCode0
Long-context Non-factoid Question Answering in Indic LanguagesCode0
Few-shot Continual Relation Extraction via Open Information Extraction—0
Testing Prompt Engineering Methods for Knowledge Extraction from TextCode0
Challenges in Expanding Portuguese Resources: A View from Open Information Extraction—0
Neon: News Entity-Interaction Extraction for Enhanced Question Answering—0
BenchIE^FL : A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark—0
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIsCode1
Large Language Models Perform on Par with Experts Identifying Mental Health Factors in Adolescent Online Forums—0
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph ConstructionCode2
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All datasetsCaRBWiRe57OIE2016BenchIELSOIE-wikiLSOIENYTPenn TreebankWebDocOIE-healthcareDocOIE-transportationCaRB OIE benchmark (Greek Use-case)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepEx (zero-shot)F185.5—Unverified
2DeepStruct multi-task w/ finetuneF145—Unverified
3DeepStruct multi-taskF143.6—Unverified
4Deepstruct zero-shotF128.9—Unverified