作者归属的神经网络

N. Tsimboukakis, G. Tambouratzis
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引用次数: 3

摘要

本文研究了神经网络模型在应用于基于作者风格的希腊语文本分类任务时的有效性。比较研究了多层感知器(MLP)、径向基函数网络(RBF)和自组织映射(SOM)在基于一组可计数文体特征的文档分类任务中的应用。这项任务对于涉及非常大的文档数据库的信息检索应用程序特别重要,在这些应用程序中,人工分类是极其劳动密集型的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Networks for Author Attribution
The present article investigates the effectiveness of neural network models when applied to the task of categorising texts in the Greek language based on the style of their authors. Multilayer perceptrons (MLP), radial basis function networks (RBF) and self-organizing maps (SOM) are comparatively studied on the task of classifying documents based on a set of countable stylistic features. This task is of particular importance for information retrieval applications that involve very large databases of documents where the manual classification is extremely labour-intensive.
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