Retrieval test evaluation of a rule based automatic indexing (AIR/PHYS)
Proc. of the third joint BCS and ACM symposium on Research and development in information retrieval
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Automatic Document Classification
Journal of the ACM (JACM)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Text classification in a hierarchical mixture model for small training sets
Proceedings of the tenth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
CONSTRUE/TIS: A System for Content-Based Indexing of a Database of News Stories
IAAI '90 Proceedings of the The Second Conference on Innovative Applications of Artificial Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
The VLDB Journal — The International Journal on Very Large Data Bases
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Filtering product reviews from web search results
Proceedings of the 2007 ACM symposium on Document engineering
Wikipedia-Based Kernels for Text Categorization
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Proceedings of the 17th international conference on World Wide Web
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Teaching applied natural language processing: triumphs and tribulations
TeachNLP '05 Proceedings of the Second ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Businesses, governments, and individuals leak confidential information, both accidentally and maliciously, at tremendous cost in money, privacy, national security, and reputation. Several security software vendors now offer "data loss prevention" (DLP) solutions that use simple algorithms, such as keyword lists and hashing, which are too coarse to capture the features what makes sensitive documents secret. In this paper, we present automatic text classification algorithms for classifying enterprise documents as either sensitive or non-sensitive. We also introduce a novel training strategy, supplement and adjust, to create a classifier that has a low false discovery rate, even when presented with documents unrelated to the enterprise. We evaluated our algorithm on several corpora that we assembled from confidential documents published on WikiLeaks and other archives. Our classifier had a false negative rate of less than 3.0% and a false discovery rate of less than 1.0% on all our tests (i.e, in a real deployment, the classifier can identify more than 97% of information leaks while raising at most 1 false alarm every 100th time).