FAQtory: A framework to provide high-quality FAQ retrieval systems
Expert Systems with Applications: An International Journal
A machine-translation method for normalization of SMS
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A high-performance FAQ retrieval method using minimal differentiator expressions
Knowledge-Based Systems
Learning regular expressions to template-based FAQ retrieval systems
Knowledge-Based Systems
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To resolve lexical disagreement problems in FAQ retrieval, we propose a high-performance FAQ retrieval system using query-log clustering. The FAQ retrieval system is divided into two subsystems: a query-log clustering system and a cluster-based retrieval system. During indexing, the query-log clustering subsystem classifies the logs of users' queries into predefined FAQ categories using a dimensionality reduction technique called latent semantic analysis. Then, it groups the query logs according to the classification results. During retrieval, the cluster-based retrieval subsystem smoothes the FAQs using the query-log clusters. Then, it calculates the similarities between the users' queries and the smoothed FAQs. Using the cluster-based retrieval technique, the proposed system can partially bridge lexical chasms between users' queries and FAQs. In addition, the proposed system outperforms the traditional information retrieval systems in FAQ retrieval.