Text Embedding for Sub-Entity Ranking from User Reviews
Authors: 
Chih-Yu Chao, Yi-Fan Chu, Hsiu-Wei Yang, Chuan-Ju Wang, 
Ming-Feng TsaiAuthors Info & Claims
Chih-Yu Chao, Yi-Fan Chu, Hsiu-Wei Yang, Chuan-Ju Wang, Ming-Feng TsaiAuthors Info & ClaimsPages 2011 - 2014
Abstract
This paper attempts to conduct analysis for one certain type of user reviews; that is, the reviews on a super-entity (e.g., restaurant) involve descriptions for many sub-entities (e.g., dishes). To deal with such analysis, we propose a text embedding framework for ranking sub-entities from user reviews of a given super-entity. Experiments on two real-world datasets show that our method outperforms three baselines by a statistically significant amount. Intriguing cases from the experiments are discussed in the paper.
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Index Terms
- Text Embedding for Sub-Entity Ranking from User Reviews
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Information & Contributors
Information
Published In
November 2017
2604 pages
ISBN:9781450349185
DOI:10.1145/3132847
- General Chairs:
- Ee-Peng Lim,
- Marianne Winslett,
- Program Chairs:
- Mark Sanderson,
- Ada Fu,
- Jimeng Sun,
- Shane Culpepper,
- Eric Lo,
- Joyce Ho,
- Debora Donato,
- Rakesh Agrawal,
- Yu Zheng,
- Carlos Castillo,
- Aixin Sun,
- Vincent S. Tseng,
- Chenliang Li
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Publication History
Published: 06 November 2017
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- Short-paper
Funding Sources
- Ministry of Science and Technology Taiwan
Conference
CIKM '17: ACM Conference on Information and Knowledge Management
November 6 - 10, 2017
Singapore, Singapore
Acceptance Rates
CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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