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首页> 外文期刊>International Journal of Applied Engineering Research >Term Expansion and Powerlabel Set for Multi-Label Hierarchical on Short Document Classification
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Term Expansion and Powerlabel Set for Multi-Label Hierarchical on Short Document Classification

机译：术语扩展和PowerLabel在短文档分类上为多标签分层设置

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摘要

The task of hierarchical classification is getting more challenging when handling short text. Short text documents contain a limited number of words which make it highly ambiguous regarding the difficulty of extracting contextual information. Several approaches have been proposed for the task of hierarchical text classification. However, such approaches have used the One-against-all mechanism which seems to be insufficient for the short text classification. Therefore, this paper aims to propose a combination of expansion method and Powerset-label mechanism for the short hierarchical classification using the Support Vector Machine (SVM) classifier. The expansion aims to handle the problem of 'something-like' that lies behind the short text by providing semantic correspondences using WordNet dictionary. On the other hand, the Powerset-label mechanism will be utilized with the SVM classifier in order to handle the problem of hierarchical text classification. To test the proposed method, a short text dataset of ACM has been used in the experiments which contain a vast amount of titles and keywords related to publication articles. Experimental results have showed that the expansion method has improved the hierarchical classification achieving an f-measure of 88.6%.
机译：分层分类的任务在处理短文本时越来越具有挑战性。短文本文档包含有限数量的单词，这使得在提取上下文信息的难度方面使其成为极常见的。已经提出了几种方法，用于分层文本分类的任务。然而，这种方法使用了唯一的所有机制，似乎不足以进行短文本分类。因此，本文旨在提出使用支持向量机（SVM）分类器的短分层分类的扩展方法和Powerset标签机制的组合。扩张旨在通过使用WordNet Dictionary提供语义对应，处理“类似”的“类似”的问题。另一方面，Powerset-Label机制将与SVM分类器一起使用，以处理分层文本分类的问题。为了测试所提出的方法，ACM的短文本数据集已用于含有大量标题和与公开文章相关的关键字的实验中。实验结果表明，膨胀方法改善了达到88.6％的衡量标准的等级分类。

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