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1 Improving Text Categorization Bootstrapping via Unsupervised Learning
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In: http://u.cs.biu.ac.il/~dagan/publications/a1-gliozzo-from journal site.pdf
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| 62 |
Extracting Relevant Named Entities for Automated Expense Reimbursement
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In: http://www.ece.umd.edu/~zhugy/EntityExtraction_Guangyu_Zhu_KDD2007.pdf
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| 63 |
Adversarial Stylometry: Circumventing Authorship Recognition to Preserve Privacy and Anonymity
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In: https://www.cs.drexel.edu/~sa499/papers/adversarial_stylometry.pdf
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Abstract:
The use of stylometry, authorship recognition through purely linguistic means, has contributed to literary, historical, and criminal investigation breakthroughs. Existing stylometry research assumes that authors have not attempted to disguise their linguistic writing style. We challenge this basic assumption of existing stylometry methodologies and present a new area of research: adversarial stylometry. Adversaries have a devastating effect on the robustness of existing classification methods. Our work presents a framework for creating adversarial passages including obfuscation, where a subject attempts to hide her identity, and imitation, where a subject attempts to frame another subject by imitating his writing style, and translation where original passages are obfuscated with machine translation services. This research demonstrates that manual circumvention methods work very well while automated translation methods are not effective. The obfuscation method reduces the techniques ’ effectiveness to the level of random guessing and the imitation attempts succeed up to 67 % of the time depending on the stylometry technique used. These results are more significant given the fact that experimental subjects were unfamiliar with stylometry, were not professional writers, and spent little time on the attacks. This article also contributes to the field by using human subjects to empirically validate the claim of high accuracy for four current techniques (without adversaries). We have also compiled and released two corpora of adversarial stylometry texts to promote research in this field with
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Keyword:
adversarial stylometry; Algorithms; anonymity ACM Reference Format; Applications; authorship recognition; Categories and Subject Descriptors; Experimentation Additional Key Words and Phrases; I.2.7 [Natural Language Processing; I.5.4 [Pattern Recognition; K.4 [Computers and Society; machine learning; privacy; Public Policy Issues—Privacy General Terms; Stylometry; Text analysis; text mining
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.370.9923
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| 64 |
Rank Learning for Factoid Question Answering with Linguistic and Semantic Constraints
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In: http://www.cs.cmu.edu/%7Embilotti/pubs/Bilotti%3ACIKM10.pdf
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| 65 |
Modeling Click-through Based Word-pairs for Web Search
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In: http://research.microsoft.com/en-us/um/people/jfgao/paper/2013/sigirfp436-Jagarlamudi.pdf
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