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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

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Front. Comput. Sci.    2014, Vol. 8 Issue (2) : 279-288    https://doi.org/10.1007/s11704-014-3043-8
RESEARCH ARTICLE
Entity attribute discovery and clustering from online reviews
Qingliang MIAO1,*(),Qiudan LI2,Daniel ZENG2,Yao MENG1,Shu ZHANG1,Hao YU3
1. Fujitsu Research & Development Center, Beijing 100025, China
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3. Ricoh Software Research Center (Beijing) Co., Ltd., Beijing 100082, China
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Abstract

The rapid increase of user-generated content (UGC) is a rich source for reputation management of entities, products, and services. Looking at online product reviews as a concrete example, in reviews, customers usually give opinions on multiple attributes of products, therefore the challenge is to automatically extract and cluster attributes that are mentioned. In this paper, we investigate efficient attribute extraction models using a semi-supervised approach. Specifically, we formulate the attribute extraction issue as a sequence labeling task and design a bootstrapped schema to train the extraction models by leveraging a small quantity of labeled reviews and a larger number of unlabeled reviews. In addition, we propose a clustering By committee (CBC) approach to cluster attributes according to their semantic similarity. Experimental results on real world datasets show that the proposed approach is effective.
Keywords opinion mining      attribute extraction      attribute clustering     
Corresponding Author(s): Qingliang MIAO   
Issue Date: 24 June 2014
 Cite this article:   
Qingliang MIAO,Qiudan LI,Daniel ZENG, et al. Entity attribute discovery and clustering from online reviews[J]. Front. Comput. Sci., 2014, 8(2): 279-288.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3043-8
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I2/279
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