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Molecules, Vol. 23, Pages 1028: ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers

Molecules, Vol. 23, Pages 1028: ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers

Molecules doi: 10.3390/molecules23051028

Authors: Yuting Xing Chengkun Wu Xi Yang Wei Wang En Zhu Jianping Yin

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Authors:   Xing, Yuting ; Wu, Chengkun ; Yang, Xi ; Wang, Wei ; Zhu, En ; Yin, Jianping
Journal:   Molecules
Volume:   23
edition:   5
Year:   2018
Pages:   1028
DOI:   10.3390/molecules23051028
Publication date:   27-Apr-2018
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