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.
@article{arxiv.1608.01068,
title = {Ranking Entity Based on Both of Word Frequency and Word Sematic Features},
author = {Xiao-Bo Jin and Guang-Gang Geng and Kaizhu Huang and Zhi-Wei Yan},
journal= {arXiv preprint arXiv:1608.01068},
year = {2016}
}
Comments
The paper decribes the apporoaches that help us to achieve the first place in Baidu Cup 2016 NLP Challenge