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
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
A principal issue is that in prior methods, the KG schema has to be included in the LLM prompt to generate valid triplets; larger and more complex schema easily exceed the LLMs' context window length.
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction
To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE.
Punctuation Restoration Improves Structure Understanding without Supervision
Unsupervised learning objectives like language modeling and de-noising constitute a significant part in producing pre-trained models that perform various downstream applications from natural language understanding to conversational tasks.
LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction
Through an analysis of entity linkability in the CaRB dataset, as well as outputs from OpenIE 4 and LOKE-GPT, we see that LOKE-GPT and the "silver" TekGen triples show that the task is significantly different in content from OIE, if not structure.
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
Open Information Extraction (OpenIE) is a fundamental yet challenging task in Natural Language Processing, which involves extracting all triples (subject, predicate, object) from a given sentence.
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words.
Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment
Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience.
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna.
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.
