论文标题
人工神经网络中的语义神圣和单词表示
Semantic Holism and Word Representations in Artificial Neural Networks
论文作者
论文摘要
人工神经网络是针对自然语言处理中许多问题的最先进解决方案。从人工神经网络代表它的方式中，我们可以从语言和意义中学到什么？从单词2VEC模型的Skip-gram变体中获得的单词表示表现出有趣的语义属性。这通常是通过指代一般分布假设来解释的，该假设指出单词的含义是由它发生的上下文给出的。我们提出了一种基于Frege的整体和功能含义方法的更具体的方法。以Tugendhat对Frege作品的正式重新解释为起点，我们证明了它类似于训练Skip-gram模型的过程，并提供了对其语义属性的可能解释。
Artificial neural networks are a state-of-the-art solution for many problems in natural language processing. What can we learn about language and meaning from the way artificial neural networks represent it? Word representations obtained from the Skip-gram variant of the word2vec model exhibit interesting semantic properties. This is usually explained by referring to the general distributional hypothesis, which states that the meaning of the word is given by the contexts where it occurs. We propose a more specific approach based on Frege's holistic and functional approach to meaning. Taking Tugendhat's formal reinterpretation of Frege's work as a starting point, we demonstrate that it is analogical to the process of training the Skip-gram model and offers a possible explanation of its semantic properties.
