Relation Extraction
718 papers with code • 50 benchmarks • 78 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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Hyper-Relational Extraction
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DrugProt
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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
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier
Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG).
Towards a scalable AI-driven framework for data-independent Cyber Threat Intelligence Information Extraction
Additionally, our supervised Entity Extractor surpasses current state-of-the-art performance in cyber Entity Extraction, highlighting the dual strength of the framework in both low-resource and data-rich environments.
CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction
Generative relation extraction (RE) commonly involves first reformulating RE as a linguistic modeling problem easily tackled with pre-trained language models (PLM) and then fine-tuning a PLM with supervised cross-entropy loss.
Rethinking Relation Extraction: Beyond Shortcuts to Generalization with a Debiased Benchmark
This paper addresses the issue of entity bias in relation extraction tasks, where models tend to rely on entity mentions rather than context.
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities
Then, we expanded the document graph into a document knowledge graph by retrieving the external knowledge base for common-sense reasoning and a novel knowledge filtration method was presented to filter out irrelevant knowledge.
AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models
This study has focused on the following major questions: (i) how to generate sentences from relation tuples, (ii) how to compare and rank them, (iii) can we combine strengths of individual methods and amalgamate them to generate an even bette quality of sentences, and (iv) how to evaluate the final dataset?
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise.
DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering
This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of "House of the Dragon" and "Game Of Thrones" TV series.
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neural network to predict the best dataset combinations.
Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences
Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks.
