TY - GEN
T1 - Investigating the Challenges of Temporal Relation Extraction from Clinical Text
AU - Galvan, Diana
AU - Okazaki, Naoaki
AU - Matsuda, Koji
AU - Inui, Kentaro
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR1513, Japan. We thank the anonymous reviewers for their insightful comments and suggestions.
Publisher Copyright:
© 2018 Association for Computational Linguistics.
PY - 2018
Y1 - 2018
N2 - 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 LSTMRNN 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.
AB - 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 LSTMRNN 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.
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M3 - Conference contribution
AN - SCOPUS:85073549595
T3 - EMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop
SP - 55
EP - 64
BT - EMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018
Y2 - 31 October 2018
ER -