Papers › REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

19 Apr 2024arXiv:2404.12788archive 2025-07-28

Nacime Bouziani, Shubhi Tyagi, Joseph Fisher, Jens Lehmann, Andrea Pierleoni

Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph. It is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual subtasks and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points. The combination of speed and accuracy makes REXEL an accurate cost-efficient system for extracting structured information at web-scale. We also release an extension of the DocRED dataset to enable benchmarking of future work on DocIE, which is available at https://github.com/amazon-science/e2e-docie.

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Tasks

BenchmarkingCoreference ResolutionDocument-level Closed Information ExtractionDocument-level Relation ExtractionEntity DisambiguationEntity LinkingEntity RetrievalEntity TypingJoint Entity and Relation ExtractionNamed Entity Recognition (NER)Relation ClassificationRelation ExtractionSentencecoreference-resolution

1 archive task tag without a task page not shown.

Datasets

Introduced by this paper, per the archive.

DocRED-IE

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Coreference Resolution DWIE REXEL Avg. F1 95.12 #1 of 3 Archive leaderboard report
Coreference Resolution DocRED-IE REXEL Avg F1 90.93 #1 of 1 Archive leaderboard report
Document-level Closed Information Extraction DWIE REXEL F1-Hard 53.77 #1 of 1 Archive leaderboard report
Document-level Closed Information Extraction DocRED REXEL Relation F1 27.96 #1 of 1 Archive leaderboard report
Document-level Closed Information Extraction DocRED-IE REXEL Relation F1 27.96 #1 of 1 Archive leaderboard report
Document-level Relation Extraction DocRED-IE REXEL Relation F1 60.10 #1 of 1 Archive leaderboard report
Entity Disambiguation DocRED-IE REXEL Avg F1 86.74 #1 of 1 Archive leaderboard report
Entity Typing DocRED-IE REXEL Avg F1 96.01 #1 of 1 Archive leaderboard report
Joint Entity and Relation Extraction DocRED REXEL Relation F1 39.06 #5 of 6 Archive leaderboard report
Joint Entity and Relation Extraction DocRED-IE REXEL Relation F1 39.06 #1 of 1 Archive leaderboard report
Named Entity Recognition (NER) DWIE REXEL F1-Hard 90.59 #1 of 3 Archive leaderboard report
Relation Extraction DWIE REXEL F1-Hard 65.8 #1 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SPEED

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