REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
arxiv(2024)
摘要
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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