CAJ | 학술논문

传统的文本分类方法需要标注好的语料来训练分类器，然而人工标记语料代价高昂并且耗时。对此，通过无类别标记的Web数据来训练文本分类器，提出一种基于无标记Web数据的层次式文本分类方法，该方法结合类别知识和主题层次信息来构造Web查询，从多种Web数据中搜索相关文档并抽取学习样本，为监督学习找到分类依据，并结合层次式支持向量机进行分类器的学习。实验结果表明，该方法能够利用无标记Web数据学习分类器，并取得了较好的分类效果，其性能接近于有标记训练样本的监督分类方法。
전통적문본분류방법수요표주호적어료래훈련분류기，연이인공표기어료대개고앙병차모시。대차，통과무유별표기적Web수거래훈련문본분류기，제출일충기우무표기Web수거적층차식문본분류방법，해방법결합유별지식화주제층차신식래구조Web사순，종다충Web수거중수색상관문당병추취학습양본，위감독학습조도분류의거，병결합층차식지지향량궤진행분류기적학습。실험결과표명，해방법능구이용무표기Web수거학습분류기，병취득료교호적분류효과，기성능접근우유표기훈련양본적감독분류방법。
Traditional text classification methods require a labeled corpus to train classifiers , however , it is costly and time-consuming to label corpus manually .This paper proposes a hierarchical text classification method , which trains the text classifier with web data that does not require any classification labels .This method constructs web in-quiry by combining classification knowledge and topic hierarchical information , searches relevant documents and extracts the learning sample from many kinds of web data , finds a classification basis to monitor the learning, and combines a hierarchical support vector machine to train classifiers .The experimental results show that this method is able to train classifiers through non-labeled web data , and gains a better result of classification with a performance that is at a level close to the supervised classification method with labeled training samples .