A Web-based information system that reasons with structured collections of text
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using background knowledge to improve text classification
Using background knowledge to improve text classification
SOPHIA-TCBR: A knowledge discovery framework for textual case-based reasoning
Knowledge-Based Systems
A Web-Based Self-training Approach for Authorship Attribution
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Using the Web as corpus for self-training text categorization
Information Retrieval
Integrating background knowledge into text classification
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Sophia: a novel approach for textual case-based reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Taking advantage of the web for text classification with imbalanced classes
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Customized travel information recommendation framework using CBR and collective intelligence
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Discovering knowledge about key sequences for indexing time series cases in medical applications
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Enhancing short text clustering with small external repositories
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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This paper describes two different approaches for incorporating background knowledge into nearest-neighbor text classification. Our first approach uses background text to assess the similarity between training and test documents rather than assessing their similarity directly. The second method redescribes examples using Latent Semantic Indexing on the background knowledge, assessing document similarities in this redescribed space. Our experimental results showthat both approaches can improve the performance of nearest-neighbor text classification. These methods are especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a specific problem on the World Wide Web.