natural-language-processing-6

Binary Relation Extraction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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BioRED

SciREX

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SciREX

BioRED

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Most implemented papers

Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019

Hinrich Schütze, Usama Yaseen, Pankaj Gupta•Mon Oct 07 2019

The proposed approach of Named Entities can be generalized to different languages and it’s effectiveness for English and Spanish text is shown, and the system ranked first in the SeeDev-binary Relation Extraction Task.

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Document-level Entity-based Extraction as Template Generation

Nanyun Peng, Kung-Hsiang Huang, Sam Tang•Thu Sep 09 2021

A generative framework is proposed for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE), allowing models to efficiently capture cross-entity dependencies, exploit label semantics, and avoid the exponential computation complexity of identifying N-ary relations.

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BioRED: a rich biomedical relation extraction dataset

Zhiyong Lu, Chih-Hsuan Wei, Ling Luo, Po-Ting Lai, C. Arighi•Thu Apr 07 2022

This work presents a first-of-its-kind biomedical relation extraction dataset (BioRED) with multiple entity types and relation pairs at the document level, enabling automated algorithms to differentiate between novel and background information.

173 0

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