Fast and scalable neural embedding models for biomedical sentence classification

Fast and scalable neural embedding models for biomedical sentence classification

Abstract Background Biomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories (e.g., Backg...

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Bibliographic Details
Main Authors: Asan Agibetov, Kathrin Blagec, Hong Xu, Matthias Samwald
Format: Article
Language:English
Published: BMC 2018-12-01
Series:BMC Bioinformatics
Subjects:
Natural language processing
Text classification
Neural networks
Word vector models
FastText
Scientific abstracts
Online Access:http://link.springer.com/article/10.1186/s12859-018-2496-4

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http://link.springer.com/article/10.1186/s12859-018-2496-4

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