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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A COMPARATIVE STUDY OF WORD SENSE DISAMBIGUATION OF ENGLISH MODAL VERB BY BP NEURAL NETWORK AND SUPPORT VECTOR MACHINE
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A COMPARATIVE STUDY OF WORD SENSE DISAMBIGUATION OF ENGLISH MODAL VERB BY BP NEURAL NETWORK AND SUPPORT VECTOR MACHINE

机译：BP神经网络与支持向量机对英语情态动词词义消歧的比较研究。

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摘要

This paper applies back propagation neural network (BP NN) and support vector machine (SVM) approaches in the word sense disambiguation (WSD) of English modal verb 'must' and compares the effects of WSD by the two models. First of all, a BP NN and a SVM for the WSD of English modal verb 'must' are established, respectively, and both reach an ideal correct disambiguation rate (98%). Then, based on the two models, a further investigation is carried out to see the influence of different features on the results of WSD of 'must'. After that, the two models are compared in two aspects: (1) the performance in disambiguating root 'must' from epistemic 'must'; (2) the performance in reflecting the influences of different linguistic (bag and relational) features on the effect of the WSD. The comparative results show that the SVM is more effective and has better generalization ability than the BP NN; however, BP NN is more suitable for investigating the influence of individual linguistic feature on the effect of WSD than SVM. These comparative results provide very useful reference for model selection for WSD and for semantic studies.
机译：本文将反向传播神经网络（BP NN）和支持向量机（SVM）方法应用于英语情态动词'must'的词义消歧（WSD）中，并比较了两种模型对WSD的影响。首先，分别为英语情态动词“ must”的WSD建立了BP NN和SVM，它们都达到了理想的正确消歧率（98％）。然后，基于这两个模型，进行了进一步的研究，以了解不同功能对“必须”的WSD结果的影响。之后，从两个方面对这两种模型进行了比较：（1）消除根源“必须”与认知“必须”的歧义表现； （2）反映不同语言（包和关系）特征对WSD效果的影响的表现。对比结果表明，SVM比BP神经网络更有效，具有更好的泛化能力。但是，与SVM相比，BP神经网络更适合于研究单个语言特征对WSD效果的影响。这些比较结果为WSD的模型选择和语义研究提供了非常有用的参考。

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