CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework

CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework
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采用MapReduce框架的基于条件随机场的并行生物医学命名实体识别算法

DOI:
10.1007/s10586-015-0426-z
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发表时间:
2015-01
期刊:
Cluster Computing
影响因子:
--
通讯作者:
李克勤
李克勤
中科院分区:
其他
文献类型:
--
作者:
ZhuoTang;LingangJiang;LiYang;KenliLi;李克勤

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随着生物医学文献的快速增长,在处理大型培训样本时,生物医学指定实体识别的模型训练时间急剧增加。如何提高生物医学大的命名实体识别的效率
As the rapid growth of the biomedical literature, the model training time in biomedical named entity recognition increases sharply when dealing with large-scale training samples. How to increase the efficiency of named entity recognition in biomedical big data becomes one of the key problems in biomedical text mining. For the purposes of improving the recognition performance and reducing the training time, this paper proposes an optimization method for two-phase recognition using conditional random fields. In the first stage, each named entity boundary is detected to distinguish all real entities. In the second stage, we label the semantic class of the entity detected. To expedite the training speed, in these two phases, we implement the model training process on a parallel optimization program framework based on MapReduce. Through dividing the training set into several parts, the iterations in the training algorithm are designed as map tasks which can be executed simultaneously in a cluster, where each map function is designed to complete the calculation of a gradient vector component for each part in the training set. Our experiments show that the proposed method in this paper can achieve high performance with short training time, which has important implications for the current biological big data processing.
DOI: 10.1016/j.jcss.2014.02.006
发表时间: 2014-08
期刊: J. Comput. Syst. Sci.
影响因子: --
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期刊: XRDS
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