Syntactic Ambiguity Resolution Using A Discrimination and Robustness Oriented Adaptive Learning Algorithm

Syntactic Ambiguity Resolution Using A Discrimination and Robustness Oriented Adaptive Learning Algorithm Tung-Hui Chiang, Yi-Chung Lin and Keh-Yih Su Department of Electrical Engineering National Tsing Hua University Hsinchu, Taiwan 300, R.O.C. E-Mail: thchiang@ ee.nthu.edu.tw Topic Area: Computational Methods (Statistical), Application (NLP) Abstract 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. 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. 1. Introduction Ambiguity resolution has long been the focus in natural language processing. Many rule-based ap- proaches have been proposed in the past, However, when applying such approaches to large scale appli- cations, they usually fail to offer satisfactory pertbr- mance. As a huge amount of fine-grained knowl- edge is required to solve the ambiguity problem, it is quite difficult for rule-based approach to acquire the huge and fine-grained knowledge, and maintain consistency among them by human [Su 90a]. Probabilistic approaches attack these problems by providing a more objective measure on the pref- erence to a given interpretation. Then, these ap- proaches acquire huge and fine grained knowledge, or parameters in statistic terms from the corpus au- tomatically. The uncertainty problem in linguistic phenomena is resolved on a more solid basis if a probabilistic approach is adopted. Moreover, the knowledge acquired by the statistical method is al- ways consistent because the knowledge is acquired by jointly considering all the data in the corpus at the same time. Hence, the time for knowledge ac- quisition and the cost to maintain consistency are significantly reduced by adopting those probabilis- tic approaches. To resolve the problems resulting from syntac- tic ambiguities, a unified statistical approach for am- biguity resolution has been proposed by Su [Su 88, 92b]. In that approach, all knowledge sources, in- cluding lexical, syntactic and semantic knowledge, are encoded by a unifiedprobabilistic score function with a uniform formulation. This uniform proba- bilistic score function has been successfully applied in spoken language processing [Su 90b, 91b, 92a] and machine translation systems [Chen 91] to in- tegrate different knowledge sources for ambiguity resolution. In implementing this unified probabilistic score function, values of score functions are estimated from the data in the training corpus. However, due to the problem of insufficiency of training data and incompleteness of model knowledge, the statistical variations between the training corpus and the real application are usually …

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