Relation Extraction
668 papers with code • 49 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
FUNSD
DocRED
WebNLG
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
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 via Single-Choice Question Answering with Large Language Models
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 Large Language Models (LLMs) 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.
GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings
First, we enrich the event embeddings with event argument and structure features by using static AMR graphs and IE graphs; Then, to jointly extract multiple event relations, we use Node Transformer and construct Task-specific Dynamic Event Graphs for each type of relation.
Pipelined Biomedical Event Extraction Rivaling Joint Learning
Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event.
