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Chinese Firewall Update
Leif Guillermo, Veronika Strnadova
This material is based upon work supported by the National Science Foundation under Grant No. IIS/REU/0755462.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation.
Chinese Firewall Update Overview Long-term goal The Present Situation Development up to present Potential Alternatives 'What To Do'
http://chinadigitaltimes.net/wp-content/uploads/2008/07/great_firewall.jpg
Overview Want:
An extensive list of possibly blacklisted keywords
A better understanding of the GFC and
internet censorship. Method(s):
Chinese Character Phonological
Similarities Chinese Character Recognition
Long-term goal The goal is to create an extensive list
of possibly blacklisted keywords Manual Labor vs. Automation with
Adaptation The Chinese Firewall, Censorship,
and you
The Present Situation Reaching Out
We've spoken with one professor and are
scheduled to speak with others Currently Working on Phonology
Mappings:
Character->Sound Sound->Character
Permutations
Character recognition still a go
Image Feature Extraction SVD
http://www.itanlp.com/images/NLPdiag.gif
Mapping Example
http://www.ofoghlu.net/log/Lanet-vi-Internet-Map.jpg
Development up to present We've read some good papers The Time Course of Graphic, Phonological, and Semantic Activation in
Chinese Character Identification. Charles A. Perfetti and Li Hai Tan. Journal of Experimental Psychology: Learning Memory, and Cognition 1998, Vol. 24, No. 1, 101-118
Uncalibrated Stereo Correspondence by Singular Value Decomposition. Maurizio Pilu. Digital Media Department. HP Laboratories Bristol. HPL-97-96. August, 1997
Originally wanted CCR, ended up with
CPAs Original CCR method should still be useful
Potential Alternatives Phonetic Alignment and Similarity
Could be more rigorous than the current phonological method but need to do more research.
Gabor Filters on different parts of Chinese
Characters.
Could potentially be as accurate if not more
accurate than Feature Extraction. Possibly not what we want. Complicated, and very math intensive.
'What to do' Currently:
Graphical<->Phonological Mappings are
almost done. Next Step:
Create an algorithm to generate possible word
permutations. Tasks:
Veronika is working on the permutation algorithm. I've been dealing with the mappings. We've both been reading/gathering information.
Anyone want to recommend one or several strategies?