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首页> 外文期刊>Procedia Computer Science >Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception
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Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception

机译:在性别欺骗的情况下,通过机器学习和神经网络算法自动识别俄语文本作者的性别

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We present the analysis of approaches to solve an author gender identification task for Russian-language texts with gender deception, using different Data-Driven models based on conventional machine learning (Support Vector Classifier, Decision Tree, Gradient Boosting) and neuronet algorithms (convolutional layers, long short-term memory layers, etc.) The source of training and testing data are collections of texts from the Gender Imitation corpus, expanded by crowd-sourcing and supplemented with files of RusProfiling and RusPersonality corpora. The reached accuracy of this task milestone is presented and discussed.
机译:我们介绍了使用基于传统机器学习(支持向量分类器,决策树,梯度提升)和神经网络算法(卷积层)的不同数据驱动模型来解决具有性别欺骗性的俄语文本作者性别识别任务的方法分析(例如长期的短期记忆层等)。培训和测试数据的来源是来自“性别模仿”语料库的文本集合,通过众包扩展并提供了RusProfiling和RusPersonality语料库文件的补充。提出并讨论了此任务里程碑所达到的准确性。

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