SciREX: Scientific Relation ExtractionNatural Language Processing Lab : final report
SciREX: Scientific Relation Extraction
Natural Language Processing Lab : final report
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23.09.2025Verlag / herausgebende Einrichtung
Bonn-Aachen International Center for Information Technology (b-it) - CAISA LabVerlagsort
BonnMetadaten
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Zitierbarer Link (Handle URI) 
https://hdl.handle.net/20.500.11811/13574
Inhalt
The rapid growth of biomedical literature makes it increasingly difficult to identify and organize meaningful knowledge. This project addresses the problem by focusing on relation extraction (RE), i.e., detecting and classifying semantic relationships between biomedical entities within scientific abstracts.
Our objective is to evaluate and compare multiple paradigms for scientific relation extraction on the BioRED dataset, a manually annotated benchmark of PubMed abstracts with diverse entities and relation types. Specifically, we investigate three complementary approaches: a classification-based model using BioBERT, a question answering formulation QA4RE, and lastly, generative models SciFive and REBEL.
The central research question guiding our study is: Which modeling paradigm offers the most effective and generalizable solution for biomedical relation extraction under the constraints of the BioRED dataset?
Our objective is to evaluate and compare multiple paradigms for scientific relation extraction on the BioRED dataset, a manually annotated benchmark of PubMed abstracts with diverse entities and relation types. Specifically, we investigate three complementary approaches: a classification-based model using BioBERT, a question answering formulation QA4RE, and lastly, generative models SciFive and REBEL.
The central research question guiding our study is: Which modeling paradigm offers the most effective and generalizable solution for biomedical relation extraction under the constraints of the BioRED dataset?
Klassifikation (DDC)
004 InformatikKarar, Sayanta; Altahan, Zyad; Aloradi, Sulaeman; Elshennawy, Abdelwahab: SciREX: Scientific Relation Extraction : Natural Language Processing Lab : final report.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/13574
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/13574
@misc{handle:20.500.11811/13574,
author = {{Sayanta Karar} and {Zyad Altahan} and {Sulaeman Aloradi} and {Abdelwahab Elshennawy}},
editor = {{Frederik Labonté}},
title = {SciREX: Scientific Relation Extraction : Natural Language Processing Lab : final report},
publisher = {Bonn-Aachen International Center for Information Technology (b-it) - CAISA Lab},
year = 2025,
month = sep,
note = {The rapid growth of biomedical literature makes it increasingly difficult to identify and organize meaningful knowledge. This project addresses the problem by focusing on relation extraction (RE), i.e., detecting and classifying semantic relationships between biomedical entities within scientific abstracts.
Our objective is to evaluate and compare multiple paradigms for scientific relation extraction on the BioRED dataset, a manually annotated benchmark of PubMed abstracts with diverse entities and relation types. Specifically, we investigate three complementary approaches: a classification-based model using BioBERT, a question answering formulation QA4RE, and lastly, generative models SciFive and REBEL.
The central research question guiding our study is: Which modeling paradigm offers the most effective and generalizable solution for biomedical relation extraction under the constraints of the BioRED dataset?},
url = {https://hdl.handle.net/20.500.11811/13574}
}
author = {{Sayanta Karar} and {Zyad Altahan} and {Sulaeman Aloradi} and {Abdelwahab Elshennawy}},
editor = {{Frederik Labonté}},
title = {SciREX: Scientific Relation Extraction : Natural Language Processing Lab : final report},
publisher = {Bonn-Aachen International Center for Information Technology (b-it) - CAISA Lab},
year = 2025,
month = sep,
note = {The rapid growth of biomedical literature makes it increasingly difficult to identify and organize meaningful knowledge. This project addresses the problem by focusing on relation extraction (RE), i.e., detecting and classifying semantic relationships between biomedical entities within scientific abstracts.
Our objective is to evaluate and compare multiple paradigms for scientific relation extraction on the BioRED dataset, a manually annotated benchmark of PubMed abstracts with diverse entities and relation types. Specifically, we investigate three complementary approaches: a classification-based model using BioBERT, a question answering formulation QA4RE, and lastly, generative models SciFive and REBEL.
The central research question guiding our study is: Which modeling paradigm offers the most effective and generalizable solution for biomedical relation extraction under the constraints of the BioRED dataset?},
url = {https://hdl.handle.net/20.500.11811/13574}
}
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