Contextual question answering for the health domain

Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword-based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health-related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in 'conversations' about their health concerns, a process that can be therapeutic in itself. The system uses community-driven question-answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system-generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state-of-the-art question answering systems such as START as well as those interactive systems from TREC.