engleski

Detecting Non-covered Questions in Frequently Asked Questions Collections

Frequently asked questions (FAQ) collections are a popular and effective way of representing information, and FAQ retrieval systems provide a natural-language interface to such collections. An important aspect of efficient and trustworthy FAQ retrieval is to maintain a low fall-out rate by detecting non-covered questions. In this paper we address the task of detecting non-covered questions. We experiment with threshold-based methods as well as unsupervised one-class and supervised binary classifiers, considering tf-idf and word embeddings text representations. Experiments, carried out on a domain-specific FAQ collection, indicate that a cluster-based model with query paraphrases outperforms threshold-based, one-class, and binary classifiers.

FAQ retrieval, Novelty detection, Question answering

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