Document classification

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Document classification/categorization is a problem in information science. The task is to assign an electronic document to one or more categories, based on its contents. Document classification tasks can be divided into two sorts: supervised document classification where some external mechanism (such as human feedback) provides information on the correct classification for documents, and unsupervised document classification, where the classification must be done entirely without reference to external information. There is also a semi-supervised document classification, where parts of the documents are labeled by the external mechanism.

Contents 1 Techniques 2 Applications 3 See also 4 Further reading

[edit] Techniques

Document classification techniques include:

• naive Bayes classifier
• tf-idf
• latent semantic indexing
• support vector machines
• artificial neural network
• kNN
• decision trees, such as ID3
• Concept Mining

and approaches based on natural language processing.

[edit] Applications

Classification techniques have been applied to spam filtering, a process which tries to discern E-mail spam messages from legitimate emails.

[edit] See also

[edit] Further reading

Publications:

• Fabrizio Sebastiani. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002 [1]
• Introduction to document classification
• Bibliography on Automated Text Categorization
• Bibliography on Query Classification
• Text Classification analysis page
• Thorsten Joachims. (1998). //Text Categorization with Support Vector Machines: Learning with Many Relevant Features.// In: Proceedings of the European Conference on Machine Learning (ECML),

Data sets:

• TechTC - Technion Repository of Text Categorization Datasets
• David D. Lewis's Datasets