Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning while filtering documents
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Phrasier: a system for interactive document retrieval using keyphrases
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Interactive Internet search: keyword, directory and query reformulation mechanisms compared
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Context-sensitive text mining and belief revision for intelligent information retrieval on the web
Web Intelligence and Agent Systems
Dynamic category profiling for text filtering and classification
Information Processing and Management: an International Journal
Enhancement of information seeking using an information needs radar model
Information Processing and Management: an International Journal
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To facilitate searching and navigation, much information has been hierarchically organized into categories with different levels of generality. However, users still suffer information overload in querying a hierarchical information space, since they often cannot make their aspects of interest precise enough. One way to alleviate the problem is to interactively identify those information categories that correspond to the users' information needs (INs). In that case, information of interest may be found in a more dedicated space (i.e. subset of categories), promoting both search precision and user satisfaction. This paper presents a technique that employs text mining to build each category's profile through which users' INs may be interactively identified. The profiles are mined incrementally so that the system may adapt to the ever-changing information space. The technique is shown to be effective in mapping users' INs to suitable categories without requiring the users to enter long queries, conduct many interactions, and suffer heavy cognitive load. It may serve as an intelligent intermediary in various applications that link users to suitable information categories and service departments.