• DocumentCode
    2501159
  • Title

    KWISC — Dependency visualization for understanding context

  • Author

    Arai, Yusuke ; Hagiwara, Masato ; Ogawa, Yasuhiro ; Toyama, Katsuhiko

  • Author_Institution
    Grad. Sch. of Inf. Sci., Nagoya Univ., Nagoya, Japan
  • fYear
    2009
  • fDate
    20-22 Oct. 2009
  • Firstpage
    163
  • Lastpage
    168
  • Abstract
    We propose a dependency visualization method for understanding context, KWISC. KWISC displays dependency between bunsetsus in Japanese sentences. In particular, it splits sentences into bunsetsus and displays them hierarchically according to the depth of dependency, to understand context. In addition, KWISC is able to align two keywords respectively, while KWIC aligns one keyword. This helps to find collocations among words. Furthermore, KWISC is able to expand and collapse bunsetsus. It can shorten distances of collocating words since it shows only the main structure of sentences by collapsing the bunsetsus. Therefore, collocating words are easy to fit on the screen, and horizontal eye movement is decreased when we look for collocations. As an evaluation experiment, we have collected pairs consisting of a bunsetsu that includes a keyword and another bunsetsu on which it depends from 207,802 sentences in the EDR corpus, and have measured the distances between them in KWISC. As a result, it is confirmed that KWISC makes the distances shorter than the conventional methods, and we have shown that KWISC makes it easy to find collocations.
  • Keywords
    data visualisation; natural language processing; KWISC; collapse bunsetsus; dependency visualization method; understanding context; Displays; Helium; Information science; Natural language processing; Sorting; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Language Processing, 2009. SNLP '09. Eighth International Symposium on
  • Conference_Location
    Bangkok
  • Print_ISBN
    978-1-4244-4138-9
  • Electronic_ISBN
    978-1-4244-4139-6
  • Type

    conf

  • DOI
    10.1109/SNLP.2009.5340926
  • Filename
    5340926