scholarly journals Development of an activation tagging system for maize

Plant Direct ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. e00118
Author(s):  
John P. Davies ◽  
Vaka S. Reddy ◽  
Xing L. Liu ◽  
Avutu S. Reddy ◽  
William Michael Ainley ◽  
...  
2007 ◽  
Vol 64 (3) ◽  
pp. 329-347 ◽  
Author(s):  
Michael A. Ayliffe ◽  
Margaret Pallotta ◽  
Peter Langridge ◽  
Anthony J. Pryor

Author(s):  
Emily Chin-Fun Chen ◽  
Hsin-Sheng Tsay ◽  
Hui-Min Ho ◽  
Sushim Kumar Gupta ◽  
Chang-Sheng Wang ◽  
...  

2009 ◽  
Vol 113 (3) ◽  
pp. 290-297 ◽  
Author(s):  
Emily Chin-Fun Chen ◽  
Yu-Hsiu Su ◽  
Selvaraju Kanagarajan ◽  
Dinesh Chandra Agrawal ◽  
Hsin-Sheng Tsay

2010 ◽  
Vol 7 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Victor Busov ◽  
Yordan Yordanov ◽  
Jiqing Gou ◽  
Richard Meilan ◽  
Cathleen Ma ◽  
...  

2012 ◽  
Vol 73 (3) ◽  
pp. 357-367 ◽  
Author(s):  
Takamitsu Waki ◽  
Shunsuke Miyashima ◽  
Miyako Nakanishi ◽  
Yoichi Ikeda ◽  
Takashi Hashimoto ◽  
...  

2010 ◽  
Vol 32 (1) ◽  
pp. 53-59
Author(s):  
Yan-Long GUAN ◽  
Wan-Sha LI ◽  
Kui-De YIN ◽  
Yong-Ping YANG ◽  
Xiang-Yang HU

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li

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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