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        <title>Open Information Extraction with Entity Focused Constraints</title>
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    <name type="personal">
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            <namePart type="family">Vlachos</namePart>
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    <abstract>Open Information Extraction (OIE) is the task of extracting tuples of the form (subject, predicate, object), without any knowledge of the type and lexical form of the predicate, the subject, or the object. In this work, we focus on improving OIE quality by exploiting domain knowledge about the subject and object. More precisely, knowing that the subjects and objects in sentences are oftentimes named entities, we explore how to inject constraints in the extraction through constrained inference and constraint-aware training. Our work leverages the state-of-the-art OpenIE6 platform, which we adapt to our setting. Through a carefully constructed training dataset and constrained training, we obtain a 29.17% F1-score improvement in the CaRB metric and a 24.37% F1-score improvement in the WIRe57 metric. Our technique has important applications – one of them is investigative journalism, where automatically extracting conflict-of-interest between scientists and funding organizations helps understand the type of relations companies engage with the scientists.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.95</identifier>
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%0 Conference Proceedings
%T Open Information Extraction with Entity Focused Constraints
%A Upadhyay, Prajna
%A Balalau, Oana
%A Manolescu, Ioana
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F upadhyay-etal-2023-open
%X Open Information Extraction (OIE) is the task of extracting tuples of the form (subject, predicate, object), without any knowledge of the type and lexical form of the predicate, the subject, or the object. In this work, we focus on improving OIE quality by exploiting domain knowledge about the subject and object. More precisely, knowing that the subjects and objects in sentences are oftentimes named entities, we explore how to inject constraints in the extraction through constrained inference and constraint-aware training. Our work leverages the state-of-the-art OpenIE6 platform, which we adapt to our setting. Through a carefully constructed training dataset and constrained training, we obtain a 29.17% F1-score improvement in the CaRB metric and a 24.37% F1-score improvement in the WIRe57 metric. Our technique has important applications – one of them is investigative journalism, where automatically extracting conflict-of-interest between scientists and funding organizations helps understand the type of relations companies engage with the scientists.
%R 10.18653/v1/2023.findings-eacl.95
%U https://aclanthology.org/2023.findings-eacl.95
%U https://doi.org/10.18653/v1/2023.findings-eacl.95
%P 1285-1296

Markdown (Informal)

[Open Information Extraction with Entity Focused Constraints](https://aclanthology.org/2023.findings-eacl.95) (Upadhyay et al., Findings 2023)

• Open Information Extraction with Entity Focused Constraints (Upadhyay et al., Findings 2023)

ACL

• Prajna Upadhyay, Oana Balalau, and Ioana Manolescu. 2023. Open Information Extraction with Entity Focused Constraints. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1285–1296, Dubrovnik, Croatia. Association for Computational Linguistics.