Online News Browsing over Interrelated Target Events

  • Yusuke Koyanagi
  • Toyohide Watanabe
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)


Recently,we can easily acquire various information frommanyWeb sites. However, it is difficult to determine the next browsing page from many Web pages. We often change the search target to narrow down the result pages found out by the existing search engine. Generally, it is not easy for a searcher to change the query before he/she knows the page content in search result perfectly. In this paper, we propose a method for specifying query terms to narrow down the pages in the search result effectively. Our method detects the representative terms of the current search target according to the currently browsed page. In order to detect the representative terms of the current search target, our method calculates the occurrences of individual terms in the currently browsed page, per constant time interval. We explain the prototype system with our method and show our experiment.


News Article Candidate Target Inverted Index Search Target Important Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Turing, M., Haake, J.M., Hannemann, J.: Hypermedia and Cognition: designing for comprehension. Communication of the ACM 38(8), 57–66 (1995)CrossRefGoogle Scholar
  2. 2.
    Sumathi, C.P., Valli, R.P., Santhanam, T.: Automatic Recommendation of Web Pages in Web Usage Mining. Int’l Journal on Computer Science and Engineering 2(9), 3046–3052 (2010)Google Scholar
  3. 3.
    He, Q., Jiang, D., Liao, Z., Hoi, S.C., Chang, K., Lim, E., Li, H.: Web Query Recommendation via Sequential Query Prediction. In: Proc. of the 25th Int’l Conf. on Data Engineering, pp. 1443–1454 (2009)Google Scholar
  4. 4.
    Kaski, S.: Dimensionality reduction by random mapping: Fast similarity computation for clustering. In: Proc. of Int’l Joint Conf. on Neural Networks, vol. 1, pp. 413–418 (1998)Google Scholar
  5. 5.
    Hu, J., Fang, L., Cao, Y., Zeng, H.J., Li, H., Yang, Q., Chen, Z.: Enhancing Text Clustering by Leveraging Wikipedia Semantics. In: Proc. of the 31st Annual Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval (2008), doi: 10.1145/1390334.1390367Google Scholar
  6. 6.
    Kudo, T., Yamamoto, K., Matsumoto, Y.: Applying Conditional Random Fields to Japanese Morphological Analysis. In: Proc. of the 2004 Conf. on Empirical Methods in Natural Language Processing, EMNLP 2004, pp. 230–237 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan

Personalised recommendations