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Scoring-Thresholding Pattern Based Text Classifier

机译:基于评分阈值模式的文本分类器

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A big challenge for classification on text is the noisy of text data. It makes classification quality low. Many classification process can be divided into two sequential steps scoring and threshold setting (thresholding). Therefore to deal with noisy data problem, it is important to describe positive feature effectively scoring and to set a suitable threshold. Most existing text classifiers do not concentrate on these two jobs. In this paper, we propose a novel text classifier with pattern-based scoring that describe positive feature effectively, followed by threshold setting. The thresholding is based on score of training set, make it is simple to implement in other scoring methods. Experiment shows that our pattern-based classifier is promising.
机译:文本分类的一大挑战是文本数据的噪声。这会使分类质量降低。许多分类过程可以分为评分和阈值设置(阈值)两个连续步骤。因此,要处理嘈杂的数据问题,重要的是有效描述正面特征评分并设置合适的阈值。现有的大多数文本分类器都不专注于这两个工作。在本文中,我们提出了一种新颖的文本分类器,该分类器具有基于模式的评分,可有效描述正面特征,然后再进行阈值设置。该阈值基于训练集的分数,使其易于在其他评分方法中实施。实验表明,基于模式的分类器是有前途的。

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