Stance classification of multi-perspective consumer health information

While search engines are effective in answering direct factual questions such as, 'What are the symptoms of a disease X?', they are not so effective in addressing complex consumer health queries, which do not have a single definitive answer, such as, 'Is treatment X effective for disease Y?'. Instead, the users are presented with a vast number of search results with often contradictory perspectives and no definitive answer. We denote such queries as Multi-Perspective Consumer Health Information (MPCHI) queries for which there is no single 'Yes or No' answer. While ascertaining the credibility of the claims requires domain expertise, an efficient categorization of the search results according to their stance (support or oppose) to the queries will help the searcher in decision making. Hence, this paper focuses on the problem of stance classification for MPCHI data at sentence level, presenting a new data set for MPCHI queries. Unlike typical debate or argumentative text, the linguistic characteristics of MPCHI is quite different, with extensive use of scientific formal language and absence of opinion bearing words. Hence, such inherently different characteristic of MPCHI text requires going beyond traditional Bag of Words (BoW) features for stance classification. Hence, we propose using a rich non-traditional set of features such as medical semantic relations, stance vectors, sentiment polarity, textual entailment, and study their impact on MPCHI stance classification using an SVM and a neural network classifier. We find that using novel non-traditional features improves MPCHI stance classification performance over traditional BoW model by 24% for the SVM classifier, and 44% for the neural network classifier respectively, for the best feature combination.