Unsupervised Extraction of Popular Product Attributes from E-Commerce Web Sites by Considering Customer Reviews
Publication in refereed journal


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其它資訊
摘要We develop an unsupervised learning framework for extracting popular product attributes from product description pages originated from different E-commerce Web sites. Unlike existing information extraction methods that do not consider the popularity of product attributes, our proposed framework is able to not only detect popular product features from a collection of customer reviews but also map these popular features to the related product attributes. One novelty of our framework is that it can bridge the vocabulary gap between the text in product description pages and the text in customer reviews. Technically, we develop a discriminative graphical model based on hidden Conditional Random Fields. As an unsupervised model, our framework can be easily applied to a variety of new domains and Web sites without the need of labeling training samples. Extensive experiments have been conducted to demonstrate the effectiveness and robustness of our framework.
著者Bing LD, Wong TL, Lam W
期刊名稱ACM Transactions on Internet Technology
詳細描述Article 12.
出版年份2016
月份4
日期1
卷號16
期次2
出版社ASSOC COMPUTING MACHINERY
國際標準期刊號1533-5399
電子國際標準期刊號1557-6051
語言英式英語
關鍵詞conditional random fields; customer reviews; Information extraction; product attribute
Web of Science 學科類別Computer Science; Computer Science, Information Systems; Computer Science, Software Engineering

上次更新時間 2021-14-04 於 23:53