Entity attribute discovery and clustering from online reviews

Qingliang Miao, Qiudan Li, Daniel Zeng, Yao Meng, Shu Zhang, Hao Yu

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)279-288
Number of pages10
JournalFrontiers of Computer Science
Volume8
Issue number2
DOIs
StatePublished - Apr 2014
Externally publishedYes

Keywords

  • attribute clustering
  • attribute extraction
  • opinion mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Entity attribute discovery and clustering from online reviews'. Together they form a unique fingerprint.

  • Cite this