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Detecting Word Substitutions in Text
August 2008 (vol. 20 no. 8)
pp. 1067-1076
ASCII Text
x 
 
SW. Fong, D. Roussinov, D.B. Skillicorn, "Detecting Word Substitutions in Text," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1067-1076, August, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.94,
author = {SW. Fong and D. Roussinov and D.B. Skillicorn},
title = {Detecting Word Substitutions in Text},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {8},
issn = {1041-4347},
year = {2008},
pages = {1067-1076},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.94},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Detecting Word Substitutions in Text
IS - 8
SN - 1041-4347
SP1067
EP1076
EPD - 1067-1076
A1 - SW. Fong,
A1 - D. Roussinov,
A1 - D.B. Skillicorn,
PY - 2008
KW - textual analysis
KW - counterterrorism
KW - word frequencies
KW - data mining
KW - pointwise mutual information
KW - co-occurrence
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Searching for words on a watchlist is one way in which large-scale surveillance of communication can be done, for example in intelligence and counterterrorism settings. One obvious defense is to replace words that might attract attention to a message with other, more innocuous, words. For example, the sentence the attack will be tomorrow" might be altered to the complex will be tomorrow", since 'complex' is a word whose frequency is close to that of 'attack'. Such substitutions are readily detectable by humans since they do not make sense. We address the problem of detecting such substitutions automatically, by looking for discrepancies between words and their contexts, and using only syntactic information. We define a set of measures, each of which is quite weak, but which together produce per-sentence detection rates around 90% with false positive rates around 10%. Rules for combining persentence detection into per-message detection can reduce the false positive and false negative rates for messages to practical levels. We test the approach using sentences from the Enron email and Brown corpora, representing informal and formal text respectively.

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Index Terms:
textual analysis, counterterrorism, word frequencies, data mining, pointwise mutual information, co-occurrence
Citation:
SW. Fong, D. Roussinov, D.B. Skillicorn, "Detecting Word Substitutions in Text," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1067-1076, Aug. 2008, doi:10.1109/TKDE.2008.94
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