Concept learning and heuristic classification in weak-theory domains
Artificial Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Introduction: Interactive Case-Based Reasoning
Applied Intelligence
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Visualizing and Evaluating Complexity of Textual Case Bases
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Robust Measures of Complexity in TCBR
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A propositional approach to textual case indexing
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Sprinkling: supervised latent semantic indexing
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
Two-part segmentation of text documents
Proceedings of the 21st ACM international conference on Information and knowledge management
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The performance of a Textual Case-Based Reasoning system is critically dependent on its underlying model of text similarity, which in turn is dependent on similarity between terms and phrases in the domain. In the absence of human intervention, term similarities are often modelled using co-occurrence statistics, which are fragile unless the corpus is truly representative of the domain. We present the case for introspective revision in TCBR, whereby the system incrementally revises its term similarity knowledge by exploiting conflicts of its representation against an alternate source of knowledge such as category knowledge in classification tasks, or linguistic and background knowledge. The advantage of such revision is that it requires no human intervention. Our experiments on classification knowledge show that revision can lead to substantial gains in classification accuracy, with results competitive to best-in-line text classifiers. We have also presented experimental results over synthetic data to suggest that the idea can be extended to improve case-base alignment in TCBR domains with textual problem and solution descriptions.