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Preference Learning for Category-Ranking based Interactive Text Categorization

机译:基于类别排名的交互式文本分类的偏好学习

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摘要

Category Ranking is a variant of the multi-label classification problem, in which, rather than performing a (hard) assignment to an object of categories from a predefined set, we rank all categories according to their estimated "degree of suitability" to the object. Category ranking has many applications, all pertaining to "interactive" classification contexts in which the system, rather than taking a final categorization decision, is simply required to support a human expert who is in charge of taking this decision. Despite its high applicative potential in information retrieval applications, and in text categorization in particular, category ranking has mainly been tackled by standard text categorization methods. In this paper, we take a radically different stand to category ranking, i.e. one in which supervision is provided to the learner not in the standard form of labels attached to training documents, but in the form of preferences of type "category c is to be preferred to category c2 for document d". We apply to this problem a recently proposed, very general model for preferential learning, and show, through experiments performed on the standard Reuters-21578 benchmark, that this largely outperforms support vector machines, the learning method which has up to now proved the best-performing one in text categorization comparative experiments.
机译:类别排名是多标签分类问题的一种变体,在该问题中,我们不是对预定义集中的类别的对象执行(硬)分配,而是根据它们对对象的估计“适合程度”对所有类别进行排名。类别排序具有许多应用程序,所有应用程序都与“交互式”分类上下文有关,在该应用程序中,仅需要系统而不是做出最终的分类决策,即可支持负责此决策的人类专家。尽管在信息检索应用中具有很高的应用潜力,尤其是在文本分类中,类别排名主要通过标准的文本分类方法来解决。在本文中,我们对类别排名采取了截然不同的立场,即对学习者的监督不是以附在培训文档上的标准标签形式,而是以“类别c是对于文档d”,首选c2类。我们将这个问题应用于最近提出的非常普遍的优先学习模型,并通过在标准Reuters-21578基准上进行的实验表明,该方法在很大程度上优于支持向量机,到目前为止,该学习方法被证明是最佳的-进行一项文本分类比较实验。

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