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Research of entity recognition method based on learning under Hadoop

机译:Hadoop下基于学习的实体识别方法研究

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Traditional entity recognition methods mainly focus on the accuracy of the analytic results for small data sets. With the coming of the era of big data, the volume of data increase sharply, traditional entity recognition method is difficult to deal with massive amounts of data sets. In order to adapt to the entity recognition of huge amounts of data, this paper proposes an entity recognition method based on learning under the background of big data in the MapReduce platform. Through the detailed analysis of the MapReduce workflow, running the algorithm based on machine learning and parallel processing data sets to identify the data entities. Experiments show that this method improves the effect of entity recognition, it has good performance and results and meets the demand for recognition of huge amounts of data entities.
机译:传统的实体识别方法主要关注小数据集分析结果的准确性。随着大数据时代的到来,数据量急剧增加,传统的实体识别方法难以处理大量的数据集。为了适应海量数据的实体识别,本文提出了一种基于学习的MapReduce平台下大数据背景下的实体识别方法。通过对MapReduce工作流程的详细分析,运行基于机器学习和并行处理数据集的算法以识别数据实体。实验表明,该方法提高了实体识别的效果,具有良好的性能和效果,满足了对大量数据实体的识别需求。

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