A Generative Dependency N-gram Language Model: Unsupervised Parameter Estimation and Application

DOI

Abstract

We design a language model based on a generative dependency structure for sentences. The parameter of the model is the probability of a dependency N-gram, which is composed of lexical words with four types of extra tag used to model the dependency relation and valence. We further propose an unsupervised expectation-maximization algorithm for parameter estimation, in which all possible dependency structures of a sentence are considered. As the algorithm is language-independent, it can be used on a raw corpus from any language, without any part-of-speech annotation, tree-bank or trained parser. We conducted experiments using four languages, i.e., English, German, Spanish and Japanese, to illustrate the applicability and the properties of the proposed approach. We further apply the proposed approach to a Chinese microblog data set to extract and investigate Internet-based, non-standard lexical dependency features of user-generated content.

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Details 詳細情報について

  • CRID
    1390001205265675648
  • NII Article ID
    130004705299
  • DOI
    10.11185/imt.9.857
  • ISSN
    18810896
  • Text Lang
    en
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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