论文标题
Cognifnn：一个模糊的神经网络框架，用于认知词嵌入评估
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation
论文作者
论文摘要
单词嵌入可以反映语义表示，并且可以通过人类自然阅读相关的认知数据源对嵌入质量进行全面评估。在本文中，我们提出了Cognifnn框架，这是使用模糊神经网络提取非线性和非平稳特征的首次尝试，以评估英语单词嵌入对相应的认知数据集。在我们的实验中，我们在三种模式中使用了15个人类认知数据集：EEG，fMRI和眼睛跟踪，并选择了均方误差和多个假设测试作为指标来评估我们提出的Cognifnn框架。与最近的先驱框架相比，我们提出的Cognifnn显示了与上下文无关（手套）和上下文敏感（BERT）单词嵌入的较小的预测错误，并具有随机生成的单词嵌入的较高的显着比率。我们的发现表明，Cognifnn框架可以对认知单词嵌入更准确，更全面的评估。这可能对外部自然语言处理任务的其他单词嵌入式评估有益。
Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In this paper, we proposed the CogniFNN framework, which is the first attempt at using fuzzy neural networks to extract non-linear and non-stationary characteristics for evaluations of English word embeddings against the corresponding cognitive datasets. In our experiment, we used 15 human cognitive datasets across three modalities: EEG, fMRI, and eye-tracking, and selected the mean square error and multiple hypotheses testing as metrics to evaluate our proposed CogniFNN framework. Compared to the recent pioneer framework, our proposed CogniFNN showed smaller prediction errors of both context-independent (GloVe) and context-sensitive (BERT) word embeddings, and achieved higher significant ratios with randomly generated word embeddings. Our findings suggested that the CogniFNN framework could provide a more accurate and comprehensive evaluation of cognitive word embeddings. It will potentially be beneficial to the further word embeddings evaluation on extrinsic natural language processing tasks.
