Exploiting Query Logs and Field-Based Models to Address Term Mismatch in an HIV/AIDS FAQ Retrieval System

One of the main challenges in the retrieval of Frequently Asked Questions (FAQ) is that the terms used by information seekers to express their information need are often different from those used in the relevant FAQ documents. This lexical disagreement (aka term mismatch) can result in a less effective ranking of the relevant FAQ documents by retrieval systems that rely on keyword matching in their weighting models. In this paper, we tackle such a lexical gap in an SMS-Based HIV/AIDS FAQ retrieval system by enriching the traditional FAQ document representation using terms from a query log, which are added as a separate field in a field-based model. We evaluate our approach using a collection of FAQ documents produced by a national health service and a corresponding query log collected over a period of 3 months. Our results suggest that by enriching the FAQ documents with additional terms from the SMS queries for which the true relevant FAQ documents are known and combining term frequencies from the different fields, the lexical mismatch problem in our system is markedly alleviated, leading to an overall improvement in the retrieval performance in terms of Mean Reciprocal Rank (MRR) and recall.