Relation Extraction
663 papers with code • 50 benchmarks • 74 datasets
Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
Source: Deep Residual Learning for Weakly-Supervised Relation Extraction
Benchmarks
These leaderboards are used to track progress in Relation Extraction
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Libraries
Use these libraries to find Relation Extraction models and implementationsDatasets
New York Times Annotated Corpus
TACRED
FewRel
DocRED
WebNLG
FUNSD
SciERC
Subtasks
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Relation Classification
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Document-level Relation Extraction
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Joint Entity and Relation Extraction
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Temporal Relation Extraction
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Temporal Relation Extraction
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Dialog Relation Extraction
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Relationship Extraction (Distant Supervised)
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Continual Relation Extraction
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Binary Relation Extraction
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Zero-shot Relation Triplet Extraction
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4-ary Relation Extraction
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DrugProt
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Hyper-Relational Extraction
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relation explanation
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Multi-Labeled Relation Extraction
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Relation Mention Extraction
Latest papers with no code
A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents
Document Understanding is an evolving field in Natural Language Processing (NLP).
Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework
Industry-wide nuclear power plant operating experience is a critical source of raw data for performing parameter estimations in reliability and risk models.
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice.
Enhancing Software Related Information Extraction with Generative Language Models through Single-Choice Question Answering
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through Generative Language Models (GLMs) using single-choice question-answering.
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation
We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKIDATA (Sorokin and Gurevych, 2017) as well as a few-shot form of the TACRED dataset (Sabo et al., 2021).
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language
We also create a manually annotated dataset with 2000 instances to evaluate the models and release it together with the dataset compiled using guided distant supervision.
MixRED: A Mix-lingual Relation Extraction Dataset
Relation extraction is a critical task in the field of natural language processing with numerous real-world applications.
CHisIEC: An Information Extraction Corpus for Ancient Chinese History
Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history.
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs
With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge.
CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction
The process of cyber mapping gives insights in relationships among financial entities and service providers.
