CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework
CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework
复制标题
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:
影响因子:
--
通讯作者:
李克勤
中科院分区:
文献类型:
--
作者:
ZhuoTang;LingangJiang;LiYang;KenliLi;李克勤
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.
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DOI:
10.1016/j.jcss.2014.02.006
发表时间:
2014-08
期刊:
J. Comput. Syst. Sci.
影响因子:
--
作者:
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通讯作者:
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DOI:
10.1145/2331042.2331060
发表时间:
2012-09
期刊:
XRDS
影响因子:
--
作者:
J. Langford
通讯作者:
J. Langford
影响因子:
2.7
作者:
Li Yang-;Yanhong Zhou
通讯作者:
Li Yang-;Yanhong Zhou
DOI:
10.3115/1567594.1567616
发表时间:
2004-08
期刊:
--
影响因子:
--
作者:
Guodong Zhou;Jian Su
通讯作者:
Guodong Zhou;Jian Su
影响因子:
2.7
作者:
LIU, DC;NOCEDAL, J
通讯作者:
NOCEDAL, J