Complementary information retrieval for cross-media news content

  • Authors:

  • Qiang Ma;Akiyo Nadamoto;Katsumi Tanaka

  • Affiliations:

  • National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto University, Kyoto, Japan

  • Venue:
  • Proceedings of the 2nd ACM international workshop on Multimedia databases
  • Year:
  • 2004

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Abstract

In this paper, we propose a new way of integrating cross-media news content, such as television programs and web pages. We search cross-media news content to find complementary items which can provide additional information to users interested in a particular topic. The complementary news items searched for are not just similar to the item the user is interested in, but also provide information in more detail or from a different perspective. First, we propose a novel content representation model called the "topic structure" model. Intuitively, a topic structure is made up of a pair of subject and content terms. Subject terms denote the dominant terms of a news item. A content term is a term having strong co-occurrence relationships with the subject terms. Based on the topic structure, we search for information related to a given news item (e. g. , one in which the user is interested) from content, context, and media complementation perspectives. We also describe an application system which concurrently presents a television news program along with complementary news articles to help users understand news topics in greater detail and from multiple perspectives.