On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data

Yaqiang Wang, Yunhui Chen, Hongping Shu, Yongguang Jiang


Abstract
High quality word embeddings are of great significance to advance applications of biomedical natural language processing. In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed. Herein, we proposed a novel approach of improving the quality of learned embeddings using out-domain data as a supplementary in the case of limited Chinese clinical records. Moreover, the embedding quality evaluation method was conducted based on Medical Conceptual Similarity Property. The experimental results revealed that selecting good training samples was necessary, and collecting right amount of out-domain data and trading off between the quality of embeddings and the training time consumption were essential factors for better embeddings.
Anthology ID:
W18-2323
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
177–182
Language:
URL:
https://aclanthology.org/W18-2323
DOI:
10.18653/v1/W18-2323
Bibkey:
Cite (ACL):
Yaqiang Wang, Yunhui Chen, Hongping Shu, and Yongguang Jiang. 2018. On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data. In Proceedings of the BioNLP 2018 workshop, pages 177–182, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data (Wang et al., 2018)
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PDF:
https://aclanthology.org/W18-2323.pdf