Text categorization by boosting automatically extracted concepts

Lijuan Cai, Thomas Hofmann
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引用次数: 144

Abstract

Term-based representations of documents have found wide-spread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage.In this paper we investigate the use of concept-based document representations to supplement word- or phrase-based features. The utilized concepts are automatically extracted from documents via probabilistic latent semantic analysis. We propose to use AdaBoost to optimally combine weak hypotheses based on both types of features. Experimental results on standard benchmarks confirm the validity of our approach, showing that AdaBoost achieves consistent improvements by including additional semantic features in the learned ensemble.
文本分类通过提升自动提取的概念
基于术语的文档表示在信息检索中得到了广泛的应用。然而,这种方法的主要缺点之一是它们在很大程度上忽略了词汇语义,因此,对于单词用法的变化来说,它们不够健壮。在本文中,我们研究了使用基于概念的文档表示来补充基于词或短语的特征。通过概率潜在语义分析,自动从文档中提取所使用的概念。我们建议使用AdaBoost对基于两种类型特征的弱假设进行最佳组合。在标准基准测试上的实验结果证实了我们方法的有效性,表明AdaBoost通过在学习的集成中包含额外的语义特征实现了一致的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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