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Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures

机译:通过使用基于Web的相似性度量来增强基于频率的方法,从而改善宽高比提取

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摘要

Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes.
机译:在线评论挖掘已用于帮助制造商和服务提供商改进其产品和服务,并为消费者的决策提供有价值的支持。产品方面的提取是在线评论挖掘的基础。这项研究旨在提高在线消费者评论中方面提取的性能。为此,我们使用PMI-IR增强了基于频率的提取方法,该方法利用Web搜索来测量方面候选对象与目标实体之间的语义相似性。此外,我们扩展了RCut(一种最初为文本分类开发的算法),以学习选择候选方面的阈值。中文在线评论的实验结果表明,我们提出的方法不仅性能优于基于频率的方面提取方法,而且可以概括不同产品领域和各种数据量。

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