Domain-specific FAQ retrieval using independent aspects

  • Authors:
  • Chung-Hsien Wu;Jui-Feng Yeh;Ming-Jun Chen

  • Affiliations:
  • National Cheng Kung University, Tainan, Taiwan;National Cheng Kung University, Tainan, Taiwan;National Cheng Kung University, Tainan, Taiwan

  • Venue:
  • ACM Transactions on Asian Language Information Processing (TALIP)
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

This investigation presents an approach to domain-specific FAQ (frequently-asked question) retrieval using independent aspects. The data analysis classifies the questions in the collected QA (question-answer) pairs into ten question types in accordance with question stems. The answers in the QA pairs are then paragraphed and clustered using latent semantic analysis and the K-means algorithm. For semantic representation of the aspects, a domain-specific ontology is constructed based on WordNet and HowNet. A probabilistic mixture model is then used to interpret the query and QA pairs based on independent aspects; hence the retrieval process can be viewed as the maximum likelihood estimation problem. The expectation-maximization (EM) algorithm is employed to estimate the optimal mixing weights in the probabilistic mixture model. Experimental results indicate that the proposed approach outperformed the FAQ-Finder system in medical FAQ retrieval.