TY - GEN
T1 - Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
AU - Liu, Yue
AU - Ge, Tao
AU - Mathews, Kusum S.
AU - Ji, Heng
AU - McGuinness, Deborah L.
N1 - Funding Information:
This work is supported by RPI's Tetherless World Constellation, IARPA FUSE Numbers D11PC20154 and J71493 and DARPA DEFT No. FA8750-13-2-0041. Dr. Mathews' effort is supported by Award #1K12HL109005-01 from the National Heart, Lung, and Blood Institute (NHLBI). The content is solely the responsibility of the authors and does not necessarily represent the official views of NHLBI, the National Institutes of Health, IARPA, or DARPA.
Funding Information:
This work is supported by RPI’s Tetherless World Constellation, IARPA FUSE Numbers D11PC20154 and J71493 and DARPA DEFT No. FA8750-13-2-0041. Dr. Mathews’ effort is supported by Award #1K12HL109005-01 from the National Heart, Lung, and Blood Institute (NHLBI). The content is solely the responsibility of the authors and does not necessarily represent the official views of NHLBI, the National Institutes of Health, IARPA, or DARPA.
Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.
AB - In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.
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M3 - Conference contribution
AN - SCOPUS:85123582458
T3 - ACL-IJCNLP 2015 - BioNLP 2015: Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop
SP - 92
EP - 97
BT - ACL-IJCNLP 2015 - BioNLP 2015
PB - Association for Computational Linguistics (ACL)
T2 - ACL-IJCNLP 2015 Workshop on Biomedical Natural Language Processing, BioNLP 2015
Y2 - 30 July 2015
ER -