Relation Extraction

637 papers with code • 50 benchmarks • 72 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


Use these libraries to find Relation Extraction models and implementations

Latest papers with no code

Creating a Fine Grained Entity Type Taxonomy Using LLMs

no code yet • 19 Feb 2024

In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy.

C-ICL: Contrastive In-context Learning for Information Extraction

no code yet • 17 Feb 2024

Recently, there has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction

no code yet • 17 Feb 2024

(2) We fine-tune a bidirectional Small Language Model (SLM) using these initial seeds to learn the relations for the target domain.

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models

no code yet • 16 Feb 2024

The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).

Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion

no code yet • 24 Jan 2024

While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms.

Distantly Supervised Morpho-Syntactic Model for Relation Extraction

no code yet • 18 Jan 2024

The task of Information Extraction (IE) involves automatically converting unstructured textual content into structured data.

MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction

no code yet • 18 Jan 2024

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc.

Dynamic Relation Transformer for Contextual Text Block Detection

no code yet • 17 Jan 2024

Contextual Text Block Detection (CTBD) is the task of identifying coherent text blocks within the complexity of natural scenes.

BERTologyNavigator: Advanced Question Answering with BERT-based Semantics

no code yet • 17 Jan 2024

The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing.

EMBRE: Entity-aware Masking for Biomedical Relation Extraction

no code yet • 15 Jan 2024

Information extraction techniques, including named entity recognition (NER) and relation extraction (RE), are crucial in many domains to support making sense of vast amounts of unstructured text data by identifying and connecting relevant information.