Context-aware web search using dynamically weighted information fusion

2011 ◽  
Vol 25 (5) ◽  
pp. 672-685 ◽  
Author(s):  
Nicole Anderson
Keyword(s):  
Information Fusion ◽  
Web Search ◽  
Context Aware

A Context-Aware Click Model for Web Search

2020 ◽  
Author(s):  
Jia Chen ◽  
Jiaxin Mao ◽  
Yiqun Liu ◽  
Min Zhang ◽  
Shaoping Ma
Keyword(s):  
Web Search ◽  
Context Aware ◽  
Click Model

Improving Search Snippets in Context-Aware Web Search Scenarios

2020 ◽  
pp. 3-16
Author(s):  
Jia Chen ◽  
Jiaxin Mao ◽  
Yiqun Liu ◽  
Min Zhang ◽  
Shaoping Ma
Keyword(s):  
Web Search ◽  
Context Aware

Context-Aware Query Refinement for Mobile Web Search

2007 ◽  
Author(s):  
Shun Hattori ◽  
Taro Tezuka ◽  
Katsumi Tanaka
Keyword(s):  
Web Search ◽  
Context Aware ◽  
Query Refinement ◽  
Mobile Web

scholarly journals Effectiveness of Context-Aware Character Input Method for Mobile Phone Based on Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Masafumi Matsuhara ◽  
Satoshi Suzuki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Phones ◽  
Web Search ◽  
Mapping Method ◽  
Context Aware ◽  
Input Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
E Mail

Opportunities and needs are increasing to input Japanese sentences on mobile phones since performance of mobile phones is improving. Applications like E-mail, Web search, and so on are widely used on mobile phones now. We need to input Japanese sentences using only 12 keys on mobile phones. We have proposed a method to input Japanese sentences on mobile phones quickly and easily. We call this method number-Kanjitranslation method. The number string inputted by a user is translated intoKanji-Kanamixed sentence in our proposed method. Number string toKanastring is a one-to-many mapping. Therefore, it is difficult to translate a number string into the correct sentence intended by the user. The proposed context-aware mapping method is able to disambiguate a number string by artificial neural network (ANN). The system is able to translate number segments into the intended words because the system becomes aware of the correspondence of number segments with Japanese words through learning by ANN. The system does not need a dictionary. We also show the effectiveness of our proposed method for practical use by the result of the evaluation experiment in Twitter data.

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