CoNLL04

Dataset Information
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Contents

Overview

The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.

Variants: CoNLL04

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
Relation Extraction ReLiK-Large ReLiK: Retrieve and LinK, Fast … 2024-07-31
Relation Extraction ASP+T0-3B Autoregressive Structured Prediction with Language … 2022-10-26
Relation Extraction TriMF A Trigger-Sense Memory Flow Framework … 2021-01-25
Relation Extraction TANL Structured Prediction as Translation between … 2021-01-14
Relation Extraction TablERT Named Entity Recognition and Relation … 2020-10-15
Relation Extraction Table-Sequence Two are Better than One: … 2020-10-08
Relation Extraction Deeper Deeper Task-Specificity Improves Joint Entity … 2020-02-15
Relation Extraction SpERT Span-based Joint Entity and Relation … 2019-09-17
Relation Extraction Relation-Metric with AT Neural Metric Learning for Fast … 2019-05-17
Relation Extraction Multi-turn QA Entity-Relation Extraction as Multi-Turn Question … 2019-05-14
Relation Extraction Biaffine attention End-to-end neural relation extraction using … 2018-12-29
Relation Extraction multi-head + AT Adversarial training for multi-context joint … 2018-08-21
Relation Extraction multi-head Joint entity recognition and relation … 2018-04-20

Research Papers

Recent papers with results on this dataset:

External Links: