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粒子群优化在自然语言处理中的文本和情感分类研究
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广东省教育厅2016年青年创新人才类项目(2016WQNCX005)


Research on the text and emotion classification of particle swarm optimization in natural language processing
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    摘要:

    为了提高自然语言处理中文本和情感分类的准确性,提出了一种基于粒子群优化支持向量机的机器学习方法。该方法通过对粒子群位置、速度和当前粒子最佳位置的不断更新来优化支持向量机的参数,从而寻找最佳支持向量机。文本和情感分类的实验结果显示:提出的粒子群优化支持向量机方法在训练速度和准确率方面表现出较好的性能。

    Abstract:

    Natural language processing technology has a very broad application prospect in people’s lives. Statisticalbased machine learning methods have become the mainstream technology in present natural language processing, in which various support vector machine techniques have been widely used. In order to improve the accuracy of text and sentiment classification in natural language processing, a machine learning method based on particle swarm optimization support vector machine is proposed. The method optimized the parameters of the support vector machine by constantly updating the position, speed, and optimal position of the current particle, so as to find the best support vector machine. Experimental results of text and sentiment classification show that the proposed particle swarm optimization support vector machine method has good performance in terms of accuracy.

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李世祥,曹艳玲.粒子群优化在自然语言处理中的文本和情感分类研究[J].机床与液压,2018,46(24):150-155.
. Research on the text and emotion classification of particle swarm optimization in natural language processing[J]. Machine Tool & Hydraulics,2018,46(24):150-155

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  • 在线发布日期: 2019-07-09
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