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A Novel Model for Recognition of Compounding Nouns in English and Chinese

机译:一种新的英汉复合名词识别模型

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Compounds are very common in many kinds of language. Most of the research in this field is from the view of morphology, while artificial neural network is seldom concerned. Based on Hopfield model, we create a novel neural network to simulate the recognition process of compounds in English and Chinese. Our model is composed of two layers: abstraction layer and recognition layer. The first layer can extract the common features of the training samples and represent it as a new attractor, which can be transferred into the next layer. This step imitates morpheme abstraction of compounds. Recognition layer is constructed as an improved Hopfield network, in which two existing attractors can merge into a new one. This step reflects the cognition of a new compound when all the morphemes are memorized. One specific example 'raincoat' is demonstrated, and the results provide strong evidence to our model.
机译:化合物在多种语言中非常常见。该领域的大多数研究是从形态学的角度,而很少涉及人工神经网络。基于Hopfield模型,我们创建了一个新颖的神经网络来模拟英语和汉语化合物的识别过程。我们的模型由两层组成:抽象层和识别层。第一层可以提取训练样本的共同特征,并将其表示为新的吸引子,可以将其转移到下一层。此步骤模仿了化合物的词素抽象。识别层构造为改进的Hopfield网络,其中两个现有吸引子可以合并为一个新吸引子。当所有语素都被记忆时,这一步骤反映了对新化合物的认知。演示了一个具体示例“雨衣”,其结果为我们的模型提供了有力的证据。

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