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    <abstract>Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE—a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes, which unifies phenomena challenging for current IE systems such as discontinuous entity spans, nested relations, variable arity n-ary relations and numeric results in a single schema. We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports. We also demonstrate the generalizability of our schema to the computer science and materials science domains. We benchmark state-of-the-art IE systems on CARE, showing that even models such as GPT4 struggle. We release our resources to advance research on extracting and aggregating literature findings.</abstract>
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%0 Conference Proceedings
%T CARE: Extracting Experimental Findings From Clinical Literature
%A Naik, Aakanksha
%A Kuehl, Bailey
%A Bransom, Erin
%A Downey, Doug
%A Hope, Tom
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F naik-etal-2024-care
%X Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE—a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes, which unifies phenomena challenging for current IE systems such as discontinuous entity spans, nested relations, variable arity n-ary relations and numeric results in a single schema. We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports. We also demonstrate the generalizability of our schema to the computer science and materials science domains. We benchmark state-of-the-art IE systems on CARE, showing that even models such as GPT4 struggle. We release our resources to advance research on extracting and aggregating literature findings.
%U https://aclanthology.org/2024.findings-naacl.285
%P 4580-4596

Markdown (Informal)

[CARE: Extracting Experimental Findings From Clinical Literature](https://aclanthology.org/2024.findings-naacl.285) (Naik et al., Findings 2024)

• CARE: Extracting Experimental Findings From Clinical Literature (Naik et al., Findings 2024)

ACL

• Aakanksha Naik, Bailey Kuehl, Erin Bransom, Doug Downey, and Tom Hope. 2024. CARE: Extracting Experimental Findings From Clinical Literature. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4580–4596, Mexico City, Mexico. Association for Computational Linguistics.