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
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction
dair-iitd/openie6
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EMNLP 2020
This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12. 3 pts improvement in F1 over previous analyzers.
Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
samuelbroscheit/open_knowledge_graph_embeddings
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ACL 2020
An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.
IMoJIE: Iterative Memory-Based Joint Open Information Extraction
While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.
CaRB: A Crowdsourced Benchmark for Open IE
We release the CaRB framework along with its crowdsourced dataset.
On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets
We present a first attempt to elucidate a theoretical and empirical approach to design the reward provided by a natural language environment to some structure learning agent.
Quantifying Similarity between Relations with Fact Distribution
thunlp/relation-similarity
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ACL 2019
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.
MinScIE: Citation-centered Open Information Extraction
Acknowledging the importance of citations in scientific literature, in this work we present MinScIE, an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations.
Improving Open Information Extraction via Iterative Rank-Aware Learning
We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences.
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.
