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Discrete and Neural Models for Chinese POS Tagging: Comparison and Combination

机译:中国POS标记的离散和神经模型:比较和组合

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摘要

Discrete and Neural models are two mainstream methods for Chinese POS tagging nowadays. Both have achieved state-of-the-art performances. In this paper, we compare the two kinds of models empirically, and further investigate the combination methods of them. In particular, as the pre-trained word embeddings are exploited under the neural setting, one can regard neural models as semi-supervised setting. To make a fairer comparison of the discrete and the neural models, we incorporate word clusters for both models as well as their combination, since it has been generally accepted that word clusters can encode similar information as pre-trained word embeddings.
机译:离散模型和神经模型是当今中国POS标记的两种主流方法。两者都取得了最先进的性能。在本文中,我们对两种模型进行了经验比较,并进一步研究了它们的组合方法。特别地,由于在神经环境下利用了预训练词嵌入,因此人们可以将神经模型视为半监督环境。为了更公平地比较离散模型和神经模型,我们将两个模型的词簇以及它们的组合合并在一起,因为人们普遍认为词簇可以将相似的信息编码为预训练词嵌入。

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