Question answering is an important sub task in the field of information retrieval. Question answering has typically used reliable sources of information such as the Wikipedia for information. In this work, we look at answering health questions using the web. The web offers the means to answer general medical questions on a variety of topics but comes with the downside of being rife with misinformation and contradictory information. We develop our techniques using the TREC health misinformation tracks that use consumer health question as topics and web crawls as their document collection.
In this work, we implement a document filtering technique based on topic-sensitive PageRank that uses a web graph of the hosts in common crawl. We develop a new passage extraction technique that performs query-based contextualized sentence selection. We test this technique on a multi-span extractive question answering dataset. We also develop an answer aggregation technique that can combine language features and manual features to predict answers to these consumer health questions. We test all of these approaches on the TREC Health Misinformation Track. We show that these techniques in the majority of cases provide an uplift in performance
