语言先验知识对神经网络模型自然语言处理任务的影响

随着互联网的发展及硬件的更新,神经网络模型被广泛应用于自然语言处理、图像识别等领域。目前,结合传统自然语言处理方法和神经网络模型正日益成为研究的热点。引入先验知识代表了传统方法的惯例,然而它们对基于神经网络模型的自然语言处理任务的影响尚不清楚。鉴于此,该文尝试探究语言层先验知识对基于神经网络模型的若干自然语言处理任务的影响。根据不同任务的特点,比较了不同先验知识和不同输入位置对不同神经网络模型的影响。通过大量的对比实验发现: 先验知识并不是对所有任务都适用,在神经网络模型的合适位置加入合适的先验知识方可加快模型的收敛速度,提高相关任务的效果。

Influence of Prior Knowledge on Neural Networks Model in NLP Tasks

The neural network model is widely used in natural language processing, image recognition and other fields. It is still an open issue whether the prior knowledge practiced by the traditional methods has impact on the task of neural network model. In this paper, we explore the influence of linguistic prior knowledge on neural network models in several NLP tasks. According to the characteristics of different tasks, we compare the effects of the different prior knowledge and the different input location on the different neural network models. Through a large number of comparative experiments, the results show that in some reasonable locations of some neural network, the prior knowledge can speed up the model’s convergence speed and improve the result, while it is not applicable for all conditions.