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2012 IEEE 12th International Conference on Data Mining Workshops
Accurate Product Name Recognition from User Generated Content
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
This paper presents the solution of the team "ISSSID" for the Consumer Products Contest #1(CPROD1) of ICDM 2012. The contest provides a dataset including hundreds of thousands of text items, a product catalog with over fifteen million products, and hundreds of manually annotated product mentions. The goal of the competition is to automatically recognize product mentions in the textual content and disambiguate which product(s) in the product catalog are referenced by the mentions. We propose a hybrid approach which combines the results obtained by several separately trained recognition models. Specifically, the approach uses a standard matching model, a rule template model, and a conditional random field model, and finally combines the results using a blending model. The proposed approach achieves the best performance in the contest.
Index Terms:
Standards,Semantics,Lead,Catalogs,Data mining,Educational institutions,Training data,CPROD1,Nature Language Processing,Named Entity Recognition
Citation:
Sen Wu, Zhanpeng Fang, Jie Tang, "Accurate Product Name Recognition from User Generated Content," icdmw, pp.874-877, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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