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        <title>Investigating the Challenges of Temporal Relation Extraction from Clinical Text</title>
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        <namePart type="given">Diana</namePart>
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    <abstract>Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solving mention-identification tasks of event and time expressions but poorly in temporal relation extraction, showing a gap of around 0.25 point below human performance. We explore to adapt the tree-based LSTM-RNN model proposed by Miwa and Bansal (2016) to temporal relation extraction from clinical text, obtaining a five point improvement over the best 2016 Clinical TempEval system and two points over the state-of-the-art. We deliver a deep analysis of the results and discuss the next step towards human-like temporal reasoning.</abstract>
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%0 Conference Proceedings
%T Investigating the Challenges of Temporal Relation Extraction from Clinical Text
%A Galvan, Diana
%A Okazaki, Naoaki
%A Matsuda, Koji
%A Inui, Kentaro
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F galvan-etal-2018-investigating
%X Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solving mention-identification tasks of event and time expressions but poorly in temporal relation extraction, showing a gap of around 0.25 point below human performance. We explore to adapt the tree-based LSTM-RNN model proposed by Miwa and Bansal (2016) to temporal relation extraction from clinical text, obtaining a five point improvement over the best 2016 Clinical TempEval system and two points over the state-of-the-art. We deliver a deep analysis of the results and discuss the next step towards human-like temporal reasoning.
%R 10.18653/v1/W18-5607
%U https://aclanthology.org/W18-5607
%U https://doi.org/10.18653/v1/W18-5607
%P 55-64

Markdown (Informal)

[Investigating the Challenges of Temporal Relation Extraction from Clinical Text](https://aclanthology.org/W18-5607) (Galvan et al., Louhi 2018)

• Investigating the Challenges of Temporal Relation Extraction from Clinical Text (Galvan et al., Louhi 2018)

ACL

• Diana Galvan, Naoaki Okazaki, Koji Matsuda, and Kentaro Inui. 2018. Investigating the Challenges of Temporal Relation Extraction from Clinical Text. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 55–64, Brussels, Belgium. Association for Computational Linguistics.