• DocumentCode
    2458695
  • Title

    AutoDict: Automated Dictionary Discovery

  • Author

    Chiang, Fei ; Andritsos, Periklis ; Zhu, Erkang ; Miller, Renée J.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Toronto, Toronto, ON, Canada
  • fYear
    2012
  • fDate
    1-5 April 2012
  • Firstpage
    1277
  • Lastpage
    1280
  • Abstract
    An attribute dictionary is a set of attributes together with a set of common values of each attribute. Such dictionaries are valuable in understanding unstructured or loosely structured textual descriptions of entity collections, such as product catalogs. Dictionaries provide the supervised data for learning product or entity descriptions. In this demonstration, we will present AutoDict, a system that analyzes input data records, and discovers high quality dictionaries using information theoretic techniques. To the best of our knowledge, AutoDict is the first end-to-end system for building attribute dictionaries. Our demonstration will showcase the different information analysis and extraction features within AutoDict, and highlight the process of generating high quality attribute dictionaries.
  • Keywords
    cataloguing; dictionaries; information retrieval; text analysis; AutoDict; attribute dictionary; automated dictionary discovery; data record; end-to-end system; entity collection; entity description; high quality dictionaries; information analysis; information extraction; information theoretic technique; learning product; loosely structured textual description; product catalog; unstructured textual description; Data mining; Data models; Dictionaries; Frequency measurement; Hidden Markov models; TV; Tagging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Engineering (ICDE), 2012 IEEE 28th International Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1063-6382
  • Print_ISBN
    978-1-4673-0042-1
  • Type

    conf

  • DOI
    10.1109/ICDE.2012.126
  • Filename
    6228187