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    <abstract>We consider the task of automatically extracting various overlapping frames, i.e, structured entities composed of multiple labels and mentions, from long clinical breast radiology documents. While many methods exist for related topics such as event extraction, slot filling, or discontinuous entity recognition, a challenge in our study resides in the fact that clinical reports typically contain overlapping frames that span multiple sentences or paragraphs. We propose a new method that addresses these difficulties and evaluate it on a new annotated corpus. Despite the small number of documents, we show that the hybridization between knowledge injection and a learning-based system allows us to quickly obtain proper results. We will also introduce the concept of scope relations and show that it both improves the performance of our system, and provides a visual explanation of the predictions.</abstract>
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%0 Conference Proceedings
%T An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports
%A Wajsburt, Perceval
%A Tannier, Xavier
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wajsburt-tannier-2023-end
%X We consider the task of automatically extracting various overlapping frames, i.e, structured entities composed of multiple labels and mentions, from long clinical breast radiology documents. While many methods exist for related topics such as event extraction, slot filling, or discontinuous entity recognition, a challenge in our study resides in the fact that clinical reports typically contain overlapping frames that span multiple sentences or paragraphs. We propose a new method that addresses these difficulties and evaluate it on a new annotated corpus. Despite the small number of documents, we show that the hybridization between knowledge injection and a learning-based system allows us to quickly obtain proper results. We will also introduce the concept of scope relations and show that it both improves the performance of our system, and provides a visual explanation of the predictions.
%R 10.18653/v1/2023.bionlp-1.13
%U https://aclanthology.org/2023.bionlp-1.13
%U https://doi.org/10.18653/v1/2023.bionlp-1.13
%P 156-170

Markdown (Informal)

[An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports](https://aclanthology.org/2023.bionlp-1.13) (Wajsburt & Tannier, BioNLP 2023)

• An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports (Wajsburt & Tannier, BioNLP 2023)

ACL

• Perceval Wajsburt and Xavier Tannier. 2023. An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 156–170, Toronto, Canada. Association for Computational Linguistics.