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Motivation

Automated Text Categorization (ATC) is the task of building software tools capable of classifying text (or hypertext) documents under predefined categories or subject codes. ATC has witnessed a booming interest in recent times, due to the availability of ever larger numbers of text documents in digital form and to the ensuing need to organize them for easier use. The dominant approach is nowadays one of building text classifiers automatically by learning the characteristics of the categories from a training set of pre-classified documents. State-of-the-art machine learning methods have recently been applied to the task, leading to systems of increased sophistication and effectiveness, and stably placing ATC at the crossroads of information retrieval and machine learning. This has encouraged the application of ATC techniques to novel domains, such as Web page categorization under hierarchical catalogues, and spoken document categorization. It also lead to the progressive adoption of automatic or semi-automatic (i.e. interactive) classification systems in applicative contexts where manual work was the rule. It is to be expected that progress in ATC will bring about systems of increased cost-effectiveness and will also constitute an important testbed for the technologies that are applied to ATC.
 

Topics of Interest

We encourage the submission of high quality, original work that has not been submitted, accepted for publication, or published elsewhere, covering any aspect of automated text categorization, including (but not restricted to) the following:

• Machine learning methods for text categorization
• Theoretical models of text categorization
• Hierarchical text categorization
• Text analysis and indexing methods for text categorization
• Dimensionality reduction for text categorization
• Evaluation issues in text categorization
• Applications of text categorization
• Automated categorization of Web pages and Web sites
• Text filtering and routing
• Topic detection and tracking
• Spoken text categorization
• OCR'ed text categorization

Submission of Manuscripts

All submissions will be reviewed on the basis of relevance, originality, significance, technical quality, and clarity. For all issues concerning length, format, and style of submissions, authors should refer to the Guidelines for Authors of Special Issues of the Journal of Intelligent Information Systems.

Papers should be submitted preferably in electronic form (only .ps or .pdf will be accepted) to either of the two guest editors. If electronic submission is not possible, please send four hard copies to either of the two guest editors.

JIIS is published by Kluwer Academic Publishers. For more information, please refer to the Kluwer JIIS homepage or to Larry Kerschberg's JIIS home page (includes information on Special Issues and Special Rate).
 

Timeline for the Special Issue

Guest Editors' addresses: