Relation Extraction
665 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
Retrieval-Augmented Generation-based Relation Extraction
To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks.
Fine-Grained Named Entities for Corona News
Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019.
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text.
Leveraging Data Augmentation for Process Information Extraction
Our study shows, that data augmentation is an important component in enabling machine learning methods for the task of business process model generation from natural language text, where currently mostly rule-based systems are still state of the art.
A Two Dimensional Feature Engineering Method for Relation Extraction
The results indicate that two-dimensional feature engineering can take advantage of a two-dimensional sentence representation and make full use of prior knowledge in traditional feature engineering.
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction
(1) The imprecision of existing evaluation metrics that struggle to effectively gauge semantic consistency between model outputs and ground truth, and (2) The inherent incompleteness of evaluation benchmarks, primarily due to restrictive human annotation schemas, resulting in underestimated LLM performances.
EGTR: Extracting Graph from Transformer for Scene Graph Generation
We propose a lightweight one-stage SGG model that extracts the relation graph from the various relationships learned in the multi-head self-attention layers of the DETR decoder.
READ: Improving Relation Extraction from an ADversarial Perspective
This strategy enables a larger attack budget for entities and coaxes the model to leverage relational patterns embedded in the context.
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
We construct the distillation dataset via sampling sentences from language model pre-training datasets (e. g., OpenWebText in our implementation) and prompting an LLM to identify the typed spans of "important information".
