Semisupervised Query Expansion with Minimal Feedback

2007 ◽  
Vol 19 (11) ◽  
pp. 1585-1589 ◽  
Author(s):  
Masayuki Okabe ◽  
Seiji Yamada
Keyword(s):  
Query Expansion

Query expansion based on global dependency parsing analysis

2013 ◽  
Author(s):  
Qinyuan Xiang ◽  
Weijiang Li ◽  
Hui Deng ◽  
Feng Wang
Keyword(s):  
Query Expansion ◽  
Dependency Parsing

Query expansion based on context of document and search result

2009 ◽  
Vol 29 (3) ◽  
pp. 852-853
Author(s):  
Hui JIANG ◽  
Xiao-hua YANG
Keyword(s):  
Query Expansion ◽  
Search Result

Name disambiguation based on query expansion

2013 ◽  
Vol 32 (9) ◽  
pp. 2488-2490
Author(s):  
Xin-xin YANG ◽  
Pei-feng LI ◽  
Qiao-ming ZHU
Keyword(s):  
Query Expansion ◽  
Name Disambiguation

Query Expansion based on Term Distribution and DBpedia Features

2021 ◽  
pp. 114909
Author(s):  
Sarah Dahir ◽  
Abderrahim El Qadi ◽  
Hamid Bennis
Keyword(s):  
Query Expansion

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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