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    <abstract>Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.</abstract>
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%0 Conference Proceedings
%T A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction
%A Lin, Chen
%A Miller, Timothy
%A Dligach, Dmitriy
%A Bethard, Steven
%A Savova, Guergana
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F lin-etal-2019-bert
%X Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.
%R 10.18653/v1/W19-1908
%U https://aclanthology.org/W19-1908
%U https://doi.org/10.18653/v1/W19-1908
%P 65-71

Markdown (Informal)

[A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction](https://aclanthology.org/W19-1908) (Lin et al., ClinicalNLP 2019)

• A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction (Lin et al., ClinicalNLP 2019)

ACL

• Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2019. A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 65–71, Minneapolis, Minnesota, USA. Association for Computational Linguistics.