scholarly journals Research on the Top-Down Parsing Method for Context-Sensitive Graph Grammars

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142776
Author(s):  
Yi Wang ◽  
XiaoQin Zeng ◽  
Han Ding
2012 ◽  
Vol 23 (7) ◽  
pp. 1635-1655 ◽  
Author(s):  
Yang ZOU ◽  
Jian LÜ ◽  
Chun CAO ◽  
Hao HU ◽  
Wei SONG ◽  
...  

2000 ◽  
Vol 23 (3) ◽  
pp. 332-333 ◽  
Author(s):  
Stephen Grossberg

The brain contains ubiquitous reciprocal bottom-up and top-down intercortical and thalamocortical pathways. These resonating feedback pathways may be essential for stable learning of speech and language codes and for context-sensitive selection and completion of noisy speech sounds and word groupings. Context-sensitive speech data, notably interword backward effects in time, have been quantitatively modeled using these concepts but not with purely feedforward models.


2009 ◽  
pp. 135-147 ◽  
Author(s):  
Yang Zou ◽  
Jian Lü ◽  
Xiaoqin Zeng ◽  
Xiaoxing Ma ◽  
Qiliang Yang

2019 ◽  
Vol 2019 (1) ◽  
pp. 15-28
Author(s):  
Yang Zou ◽  
Xiaoqin Zeng ◽  
Yufeng Liu ◽  
Huiyi Liu

Author(s):  
Felice Mancini ◽  
Daniel Grande ◽  
Pradeep Radhakrishnan

This paper explores the concept of an automated virtual lab in the area of system design and analysis. The project combines different research activities in automated design analysis using the graph grammar and tree search methods. In particular, a graph grammar rule-based system to automatically generate bond graphs for various systems is developed. This is combined with similar grammar based rules and search algorithms to provide automation as well as context sensitive feedback to users of the virtual lab. Examples will be demonstrated to showcase the potential as well as how the virtual lab can be scaled using appropriate learning algorithms towards personalizing education.


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