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Supervised author recognition with aggregated word embeddings

机译：监督作者认可与汇总单词嵌入式

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摘要

The number of texts has been remarkably increased with each passing day due to the rapid development of technology. This situation creates a need for the development of new techniques in the fields of text mining and natural language processing. Highly successful methods are developed by especially using word embedding based on artificial neural network. In this paper, an application is produced by using Word2vFisher based on word embedding and Fisher vector for the analysis of Turkish texts. A dataset containing 237 different columnist are created by collecting columns of last 20 years from the electronic archive of Hurriyet and Sabah newspapers. One of the important points of this study is that the experiments are conducted on the largest-ever dataset that contains Turkish newspaper columns. The effectiveness of the method on analysis of the Turkish texts is another important point of this study. It is believed that the method can be utilized in many other domains.
机译：由于技术的快速发展，每次过去的一天，文本数量显着增加。这种情况创造了在文本挖掘和自然语言处理领域开发新技术的需求。通过特别使用基于人工神经网络的单词嵌入来开发高度成功的方法。在本文中，通过基于Word Embedding和Fisher向量来使用Word2Vfisher来生产应用程序，以便分析土耳其语文本。包含237个不同列师的数据集是通过从Hurriyet和Sabah报纸的电子档案中收集过去20年的列来创建。本研究的重要几点之一是，实验是在含有土耳其报纸专栏的最大的数据集中进行的。该方法对土耳其语文本分析的有效性是这项研究的另一个重要观点。据信，该方法可以在许多其他域中使用。

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