Question retrieval with user intent

Abstract

Community Question Answering (CQA) services, such as Yahoo! Answers and WikiAnswers, have become popular with users as one of the central paradigms for satisfying users' information needs. The task of question retrieval in CQA aims to resolve one's query directly by finding the most relevant questions (together with their answers) from an archive of past questions. However, as users can ask any question that they like, a large number of questions in CQA are not about objective (factual) knowledge, but about subjective (sentiment-based) opinions or social interactions. The inhomogeneous nature of CQA leads to reduced performance of standard retrieval models. To address this problem, we present a hybrid approach that blends several language modelling techniques for question retrieval, namely, the classic (query-likelihood) language model, the state-of-the-art translation-based language model, and our proposed intent-based language model. The user intent of each candidate question (objective/subjective/social) is given by a probabilistic classifier which makes use of both textual features and metadata features. Our experiments on two real-world datasets show that our approach can significantly outperform existing ones.