Paper: Syntactic Ambiguity Resolution Using A Discrimination And Robustness Oriented Adaptive Learning Algorithm

ACL ID C92-1055
Title Syntactic Ambiguity Resolution Using A Discrimination And Robustness Oriented Adaptive Learning Algorithm
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1992
Authors

In this paper, a discrimination and robusmess 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 re- solve 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 possi- ble variations between the training corpus and the real tasks are als0 taken into consideration by en- larging the separation margin between the correct candidate and its competing members. Signif...