路透社数据库词性标注

R. Cretulescu, A. David, D. Morariu, L. Vintan
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引用次数: 3

摘要

即使信息检索系统中用于文档表示的向量空间模型集成了少量的知识,由于其计算成本、执行速度和简单性,它仍被继续使用。我们尝试通过添加一些语法信息(如词性)来改进这种文档表示。在本文中,我们评估了三种不同的标记算法,以便选择最合适的标记器来使用它来标记路透社数据集。在这项工作中,我们仅使用五种不同的词性来评估标注器:名词、动词、副词、形容词和其他。我们认为这些特殊的标签是将文档描述到这些部分语音空间的最具代表性的标签。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Part-of-speech labeling for Reuters database
Even if the Vector Space Model used for document representation in information retrieval systems integrates a small quantity of knowledge it continues to be used due to its computational cost, speed execution and simplicity. We try to improve this document representation by adding some syntactic information such as the parts of speech. In this paper, we have evaluated three different tagging algorithms in order to select the most suitable tagger for using it to tag the Reuters dataset. In this work, we have evaluated the taggers using only five different parts of speech: noun, verb, adverb, adjective and others. We considered these particular tags being the most representative for describing the documents into these parts of speech space.
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