Domain-specific FAQ retrieval using independent aspects

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.

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