A weighted string pattern matching-based passage ranking algorithm for video question answering

  • Authors:

  • Yu-Chieh Wu;Jie-Chi Yang;Yue-Shi Lee

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Central University, 300 Jhongda Rd., Jhongli City, Taoyuan 32001, Taiwan;Graduate Institute of Network Learning Technology, National Central University, 300 Jhongda Rd., Jhongli City, Taoyuan 32001, Taiwan;Department of Computer Science and Information Engineering, Ming Chuan University, 5 De-Ming Rd., Gwei Shan District, Taoyuan 333, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

Video question answering aims to pinpoint answers in response to user's specified questions. However, most question answering technologies involve in integrating rich specific external knowledge such as syntactic parsers, which are often unavailable for many languages. In this paper, we present a new string pattern matching-based passage ranking algorithm for extending traditional text Q/A toward videoQ/A. Users interact with our videoQ/A system through natural language questions whereas our system returns three sentence-length passages with corresponding video clips as answers. We collect 45GB Discovery videos and 253 Chinese questions for evaluation. The experimental results showed that our method outperformed six top-performed ranking models. It is 7.39% better than the second best method (language model-based) in relatively MRR score and 6.12% in precision rate. Besides, we also show that the use of a trained Chinese word segmentation tool did decrease the overall videoQ/A performance where most ranking algorithms dropped at least 10% in relatively MRR, precision, and answer pattern recall rates.