神经网络模型近些年在关系抽取任务上已经展示出了很好的效果,然而我们对于特征提取的过程所知甚少,而这也进一步限制了深度神经网络模型在关系抽取任务上的进一步发展。当前已有研究工作对英文关系抽取的语言学特征进行探究,并且得到了一些规律。然而由于中文与西方语言之间明显的差异性,其所探究到的规律与解释性不适用于中文关系抽取。本文首次对中文关系抽取神经网络进行探究,采用了四个角度共13种探究任务,其中包含中文特有的分词探究任务。在两个关系抽取数据集上进行了实验,探究了中文关系抽取模型进行特征提取的规律。

Metaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in ‘sweet voice’. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset–the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.