• DocumentCode
    3102358
  • Title

    Semantic Role Based Tamil Sentence Generator

  • Author

    Pandian, S. Lakshmana ; Geetha, T.V.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Anna Univ., Chennai, India
  • fYear
    2009
  • fDate
    7-9 Dec. 2009
  • Firstpage
    80
  • Lastpage
    85
  • Abstract
    A Machine learning technique called memory based bigram models are developed for a system that generate a simple sentence for Tamil language from a set of concept terms and their semantic role. This system consists of a learner to learn how to realize a sentence from the content of semantic role information. This learner has been designed as a statistical model that is formulated from a preprocessed corpus of sentences. This preprocessing work is handled by annotating the corpus using part of speech tagging, chunking and semantic role labeling processes. This collective annotated corpus is statistically analyzed and developed the memory based bigram models. These models thus obtained are capable of producing the appropriate sequence of semantic roles of the concept terms for realizing sentence. A phrase generator has been developed to generate the appropriate phrases involved in sentence generation.
  • Keywords
    learning (artificial intelligence); natural language processing; speech processing; statistical analysis; Tamil language; machine learning technique; memory based bigram models; semantic role based Tamil sentence generator; semantic role labeling processes; speech chunking; speech tagging; statistical model; Artificial intelligence; Computational linguistics; Computer science; Labeling; Machine learning; Natural languages; Performance analysis; Speech processing; Tagging; Weather forecasting; Conditional random field models; Machine Learning; Natural language Generation; Part of speech; chunking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asian Language Processing, 2009. IALP '09. International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-0-7695-3904-1
  • Type

    conf

  • DOI
    10.1109/IALP.2009.26
  • Filename
    5380775