Mirror on the Wall: Finding Similar Questions with Deep Structured Topic Modeling

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• 关键词：Community question answering ; Machine learning ; Deep neural networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9652
• 期：1
• 页码：454-465
• 全文大小：437 KB
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• 作者单位：Arpita Das (19)
  Manish Shrivastava (19)
  Manoj Chinnakotla (20)
  
  19. International Institute of Information Technology, Hyderabad, India
  20. Microsoft, Hyderabad, India
  
• 丛书名：Advances in Knowledge Discovery and Data Mining
• ISBN：978-3-319-31750-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Internet users today prefer getting precise answers to their questions rather than sifting through a bunch of relevant documents provided by search engines. This has led to the huge popularity of Community Question Answering (cQA) services like Yahoo! Answers, Baidu Zhidao, Quora, StackOverflow etc., where forum users respond to questions with precise answers. Over time, such cQA archives become rich repositories of knowledge encoded in the form of questions and user generated answers. In cQA archives, retrieval of similar questions, which have already been answered in some form, is important for improving the effectiveness of such forums. The main challenge while retrieving similar questions is the “lexico-syntactic” gap between the user query and the questions already present in the forum. In this paper, we propose a novel approach called “Deep Structured Topic Model (DSTM)” to bridge the lexico-syntactic gap between the question posed by the user and forum questions. DSTM employs a two-step process consisting of initially retrieving similar questions that lie in the vicinity of the query and latent topic vector space and then re-ranking them using a deep layered semantic model. Experiments on large scale real-life cQA dataset show that our approach outperforms the state-of-the-art translation and topic based baseline approaches.