Modern neural networks (NNs), trained on extensive raw sentence data, construct distributed representations by compressing individual words into dense, continuous, high-dimensional vectors. These representations are expected to capture multi-level lexical meaning. In this thesis, our objective is to examine the efficacy of distributed representations from NNs in encoding lexical meaning. Initially, we identify and formalize three levels of lexical semantics: \textit{local}, \textit{global}, and \textit{mixed} levels. Then, for each level, we evaluate language models by collecting or constructing multilingual datasets, leveraging various language models, and employing linguistic analysis theories. This thesis builds a bridge between computational models and lexical semantics, aiming to complement each other.

翻译：现代神经网络通过在大规模原始句子数据上进行训练，将单个词汇压缩为稠密、连续且高维的向量，从而构建分布式表征。这些表征预期能够捕捉多层次词汇意义。本论文旨在检验神经网络生成的分布式表征在编码词汇意义方面的有效性。首先，我们识别并形式化词汇语义的三个层次：\textit{局部}、\textit{全局}及\textit{混合}层次。随后，针对每一层次，我们通过收集或构建多语言数据集、利用多种语言模型并运用语言学分析理论来评估语言模型。本论文在计算模型与词汇语义学之间架设桥梁，旨在促进两者的互补发展。