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        <title>Joint Entity Extraction and Assertion Detection for Clinical Text</title>
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    <name type="personal">
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    <abstract>Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER)and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.</abstract>
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%0 Conference Proceedings
%T Joint Entity Extraction and Assertion Detection for Clinical Text
%A Bhatia, Parminder
%A Celikkaya, Busra
%A Khalilia, Mohammed
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bhatia-etal-2019-joint
%X Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER)and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.
%R 10.18653/v1/P19-1091
%U https://aclanthology.org/P19-1091
%U https://doi.org/10.18653/v1/P19-1091
%P 954-959

Markdown (Informal)

[Joint Entity Extraction and Assertion Detection for Clinical Text](https://aclanthology.org/P19-1091) (Bhatia et al., ACL 2019)

• Joint Entity Extraction and Assertion Detection for Clinical Text (Bhatia et al., ACL 2019)

ACL

• Parminder Bhatia, Busra Celikkaya, and Mohammed Khalilia. 2019. Joint Entity Extraction and Assertion Detection for Clinical Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 954–959, Florence, Italy. Association for Computational Linguistics.