The synergistic application of CBR to IR
Artificial Intelligence Review
Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
CBR for Document Retrieval: The FALLQ Project
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Pragmatic text mining: minimizing human effort to quantify many issues in call logs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Enabling analysts in managed services for CRM analytics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM conference on Information and knowledge management
Knowledge sciences in services automation: integration models and perspectives for service centers
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
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Modern day service centers are the building blocks for highly efficient and productive business systems in a knowledge economy. In these service systems, accurate and timely delivery of pertinent information to service representatives becomes the cornerstone for delivering efficient customer service. There are two main steps in achieving this objective. The first step concerns efficient text mining to extract critical and pertinent information from the very long service request (SR) documents in the historical database. The second step concerns matching new service requests with previously stored service requests. Both lead to efficiencies by minimizing time spent by service personnel in extracting Intellectual Capital (IC). In this paper we present our text analytics system, the Service Request Analyzer and Recommender (SRAR), which is designed to improve the productivity in an enterprise service center for computer network diagnostics and support. SRAR unifies a text preprocessor, a hierarchical classifier, and a service request recommender, to deliver critical, pertinent, and categorized knowledge for improved service efficiency. The novel feature we report here is identifying the components of the diagnostic process underlying the creation of the original text documents. This identification is crucial in the successful design and prototyping of SRAR and its hierarchical classifier element. Equally, the use of domain knowledge and human expertise to generate features are indispensable synergistic elements in improving the accuracy of the text analysis toward identifying the components of the diagnostic process. The evaluation and comparison of SRAR with other benchmark approaches in the literature demonstrate the effectiveness of our framework and algorithms. This framework can be generalized to be applicable in many service industries and business functions that mine textual data to achieve increased efficiency in their service delivery. We observe significant service time responsiveness improvements during the first step of IC extraction in network service center context at Cisco.