Automatic Word Clustering for Text Categorization Using Global Information

2005 ◽  
pp. 1-11 ◽  
Author(s):  
Chen Wenliang ◽  
Chang Xingzhi ◽  
Wang Huizhen ◽  
Zhu Jingbo ◽  
Yao Tianshun
Keyword(s):  
Text Categorization ◽  
Global Information ◽  
Word Clustering

Chinese Text Categorization via Bottom-Up Weighted Word Clustering

2015 ◽  
Vol 11 (1) ◽  
pp. 50-61
Author(s):  
Yu-Chieh Wu
Keyword(s):  
Chinese Text ◽  
Conceptual Knowledge ◽  
Text Categorization ◽  
Good Method ◽  
Latent Semantic Indexing ◽  
Test Collection ◽  
Bag Of Words ◽  
Efficiency And Effectiveness ◽  
Word Clustering ◽  
Term Clustering

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. The authors used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, they present a modify indexing with terms in cluster. Their test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.

Do You Want to See the Tree? Ignore the Forest

2014 ◽  
Vol 61 (3) ◽  
pp. 205-214 ◽  
Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  
Keyword(s):  
Visual Recognition ◽  
Local Level ◽  
Local Information ◽  
Visual Environment ◽  
Global Information ◽  
Focus Attention ◽  
Global Level ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

Global Information Inequalities

2008 ◽  
Author(s):  
Deborah H Charbonneau
Keyword(s):  
Global Information ◽  
Information Inequalities
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