QALink: Enriching Text Documents with Relevant Q&A Site Contents

Yixuan Tang, Weilong Huang, Qi Liu, Anthony KH Tung, Xiaoli Wang, Jisong Yang, Beibei Zhang

Abstract: With rapid development of Q&A sites such as Quora and StackExchange, high quality question-answer pairs have been produced by users. These Q&A contents cover a wide range of topics, and they are useful for users to resolve queries and obtain new knowledge. Meanwhile, when people are reading digital documents, they may encounter reading problems such as lack of background information and unclear illustration of concepts. We believe that Q&A sites offer high-quality contents which can serve as rich supplements to digital documents. In this paper, we devise a rigorous formulation of the novel text enrichment problem, and design an end-to-end system named QALink which assigns the most relevant Q&A contents to the corresponding section of the document. We first present a new segmentation approach to model each document with a hierarchical structure. Based on the hierarchy, queries are constructed to retrieve and rank related question-answer pairs. Both syntactical and semantic features are adopted in our system. The empirical evaluation results indicate that QALink is able to effectively enrich text documents with relevant Q&A contents to help people better understand the documents.