Question routing in community question answering: putting category in its place

  • Authors:
  • Baichuan Li;Irwin King;Michael R. Lyu

  • Affiliations:
  • The Chinese University of Hong Kong, Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

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Abstract

This paper investigates a ground-breaking incorporation of question category to Question Routing (QR) in Community Question Answering (CQA) services. The incorporation of question category was designed to estimate answerer expertise for routing questions to potential answerers. Two category-sensitive Language Models (LMs) were developed with large-scale real world data sets being experimented. Results demonstrated that higher accuracies of routing questions with lower computational costs were achieved, relative to traditional Query Likelihood LM (QLLM), state-of-the-art Cluster-Based LM (CBLM) and the mixture of Latent Dirichlet Allocation and QLLM (LDALM).