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Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on Community-based Question Answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.

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Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer-generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets 1 used in this work, one is of similar product question pairs, and the second is of product question-answer pairs. * Work carried out during an internship at Amazon. † Work carried out while working at Amazon.

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