The Complex-feature-based Model for Acquisition of VN-construction Structure Templates
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    Abstract:

    In this paper, a complex-feature- and MDL-based model for acquisition of VN-construction structure templates is put forward. First, a verb classification tree is created using statistical decision tree model. Then, the tree is pruned based on MDL (minimum description length) principle. Finally, structure templates are derived based on the verb classification tree. The experiments show that using the structure templates acquired with the model to recognizing VN-structure, the system has its advantages over the model based on the sense and the MLE (maximum likelihood estimation) principle in precision and recall.

    Reference
    [1]梅家驹等.同义词词林.上海:上海辞书出版社,1983(Mei Jia-ju et al. Tongyici Cilin. Shanghai: Shanghai Dictionary Press, 1983)
    [2]Brown P F et al.Class-based n-gram Models of natural language. Computational Lingustics, 1992,18(4):467~479
    [3]Dagan I et al. Contextual word similarity and estimation from sparse data. Computer Speech and Language, 1995,9(2):123~152
    [4]Li Hang et al. Clustering words with the MDL principle. In: Proceedings of the 16th International Conference on Com-putational Linguistics. Copenhagen, Denmark: the Association for International Computational Linguistics. 1996
    [5]Pereira F et al. Distributional clustering of English words. In: Proceedings of the 31th Annual Meeting of the Association for Computational Linguistics. Ohio, USA: Association for Computational Linguistics, 1993. 183~190
    [6]Magerman S F. Natural language parsing as statistical pattern recognition [Ph. D. Dissertation]. Stanford: Stanford University, 1994
    [7]Stanley F Chen. Building probabilistic models for natural language [Ph. D. Dissertation]. Cambridge, Massachusetts: Harvard University, 1996
    [8]Quinlan J R. Inferring decision trees using the minimum description length principle. Information and Computation, 1989,80(2):227~148
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赵 军,黄昌宁.基于复杂特征的VN结构模板获取模型*.软件学报,1999,10(1):92-99

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History
  • Received:August 14,1997
  • Revised:January 23,1998
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