A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Semantic and syntactic aspects of score function
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Robust learning, smoothing, and parameter tying on syntactic ambiguity resolution
Computational Linguistics
GPSM: a Generaized Probabilistic Semantic Model for ambiguity resolution
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Automatic model refinement: with an application to tagging
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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In this paper, a discrimination and robustness oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution. Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. The proposed method remedies these problems by adjusting the parameters to maximize the accuracy rate directly. To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members. Significant improvement has been observed in the test. The accuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach.