Community question answering is the task of answering questions on a Q&A forum or board, such as Stack Overflow or Quora.
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days.
Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo!
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data.
Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e. g., question, answer, user.
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims.
In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem.