An FAQ Search Method Using a Document Classifier Trained With Automatically Generated Training Data

Abstract

We propose an Frequently Asked Question (FAQ) search method that uses a document classifier for classifying a natural language query to a corresponding FAQ. The document classifier classifies a query with words that occur in the query. However, since FAQs have little redundancy, using FAQs as training data for the document classifier is not sufficient for classifying queries that have the similar meaning but different surface expressions. To tackle this problem, our method generates training data automatically from FAQs and corresponding histories and trains the document classifier with them. Furthermore, with the automatically generated training data, our method learns a ranking model that uses classification results of the document classifier. Experimental results on a company FAQs and corresponding histories showed that our method outperformed pseudo-relevance feedback and query expansion model that uses word alignment model in statistical machine translation.