ACM SIGIR Forum
Modern Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Guest Editors' Introduction: Intelligent Information Retrieval
IEEE Intelligent Systems
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An empirical study of three machine learning methods for spam filtering
Knowledge-Based Systems
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The automatic creation of literature abstracts
IBM Journal of Research and Development
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Much of the information used by an organization is collected in the form of manuals, regulations, news etc. These are grouped into controlled documentary collections, which are normally digitized and accessible via a content management system. However, obtaining new knowledge from collected documents in an organization requires not only sound search and retrieval of information tools, but also the techniques to establish relationships, discover patterns and provide overall descriptions of the entire contents of the collection. This article explores the nature of knowledge and the role that occupy the documentary collections as a source of obtaining him knowledge. It also describes the collection of documents will be used along the exposure of this study and the techniques of processing information in order to obtain the desired results. This paper describes the use of computational methods, support vector machines in particular, in a large organisation for document classification.