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Ranking Entity Based on Both of Word Frequency and Word Sematic Features
August 03, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Xiao-Bo Jin, Guang-Gang Geng, Kaizhu Huang, Zhi-Wei Yan
arXiv ID
1608.01068
Category
cs.IR: Information Retrieval
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Entity search is a new application meeting either precise or vague requirements from the search engines users. Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search. We achieved the first place with the average MAP scores on 4 tasks including movie, tvShow, celebrity and restaurant. In this paper, we propose a series of similarity features based on both of the word frequency features and the word semantic features and describe our ranking architecture and experiment details.
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