Ranking Entity Based on Both of Word Frequency and Word Sematic Features
This work addresses entity search for search engine users, but it is incremental as it builds on existing ranking methods with hybrid features.
The authors tackled the entity search problem by proposing similarity features based on word frequency and semantic features, achieving first place in the Baidu Cup 2016 Challenge with average MAP scores across four tasks.
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.