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 with no code
PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora
Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training.
Enriching Relation Extraction with OpenIE
Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses).
Joint Open Knowledge Base Canonicalization and Linking
However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts.
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
To validate our theoretical analysis, we further propose another method using our proposed Causality-Aware Self-Attention Mechanism (CASAM) to guide the model to learn the underlying causality knowledge in legal texts.
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models
Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs.
Open Information Extraction from 2007 to 2022 -- A Survey
Open information extraction is an important NLP task that targets extracting structured information from unstructured text without limitations on the relation type or the domain of the text.
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Enhanced Knowledge Graphs Using Typed Entailment Graphs
Constructing knowledge graphs from open-domain corpora is a crucial stage in question answering.
