Concept extraction and e-commerce applications

被引：9
|
作者：
Zhang, Yongzheng [1 ]
Mukherjee, Rajyashree [1 ]
Soetarman, Benny [1 ]
机构：
[1] eBay Inc, San Jose, CA 95125 USA
来源：
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS | 2013年 / 12卷 / 04期
关键词：
Concept extraction; Automatic keyphrase extraction; e-Commerce; Product matching; Topic-based opinion mining;
D O I：
10.1016/j.elerap.2013.03.008
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Concept extraction is the technique of mining the most important topic of a document. In the e-commerce context, concept extraction can be used to identify what a shopping related Web page is talking about. This is practically useful in applications like search relevance and product matching. In this paper, we investigate two concept extraction methods: Automatic Concept Extractor (ACE) and Automatic Keyphrase Extraction (KEA). ACE is an unsupervised method that looks at both text and HTML tags. We upgrade ACE into Improved Concept Extractor (ICE) with significant improvements. KEA is a supervised learning system. We evaluate the methods by comparing automatically generated concepts to a gold standard. The experimental results demonstrate that ICE significantly outperforms ACE and also outperforms KEA in concept extraction. To demonstrate the practical use of concept extraction in the e-commerce context, we use ICE and KEA to showcase two e-commerce applications, i.e. product matching and topic-based opinion mining. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：289 / 296
页数：8
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