<p>Using domain-specific vocabulary to detect multiple-word phrases to improve Word2Vec embedding performance in medical literature</p>

Attayeb Mohsen; Yi-An Chen; Kenji Mizuguchi

doi:10.7490/f1000research.1117647.1

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Mohsen A, Chen YA and Mizuguchi K. Using domain-specific vocabulary to detect multiple-word phrases to improve Word2Vec embedding performance in medical literature [version 1; not peer reviewed]. F1000Research 2019, 8:1933 (poster) (https://doi.org/10.7490/f1000research.1117647.1)

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Using domain-specific vocabulary to detect multiple-word phrases to improve Word2Vec embedding performance in medical literature

Attayeb Mohsen¹, Yi-An Chen, Kenji Mizuguchi

Published 19 Nov 2019 (https://doi.org/10.7490/f1000research.1117647.1)

Author Affiliations

¹ NIBIOHN, Japan

Copyright © 2019 Mohsen A et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 19 Nov 2019

Using domain-specific vocabulary to detect multiple-word phrases to improve Word2Vec embedding performance in medical literature
[version 1; not peer reviewed]

Attayeb Mohsen¹, Yi-An Chen, Kenji Mizuguchi

Author Affiliations

¹ NIBIOHN, Japan

Presented at

Japanese Chem-Bio Informatics Society (CBI) Annual Meeting 2018

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Word2vec, Deep learning, Medical literature

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