Text Embedding for Sub-Entity Ranking from User Reviews

被引：1

作者：

Chao, Chih-Yu ^{[1
]}

Chu, Yi-Fan ^{[1
]}

Yang, Hsiu-Wei ^{[2
]}

Wang, Chuan-Ju ^{[2
]}

Tsai, Ming-Feng ^{[3
]}

机构：

[1] Univ Taipei, Dept Comp Sci, Taipei, Taiwan

[2] Acad Sinica, Res Ctr Informat Technol Innovat, Taipei, Taiwan

[3] Natl Chengchi Univ, Dept Comp Sci, Taipei, Taiwan

来源：

CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT | 2017年

关键词：

text embedding; co-occurrence network; user reviews; ranking;

D O I：

10.1145/3132847.3133066

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2011 / 2014

页数：4

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