语义选择限制刻画谓语对论元的语义选择倾向,对自然语言的句法语义分析有重要作用,语义选择限制知识的自动获取也成为一个重要的研究课题。鉴于神经网络模型在自然语言处理的很多任务中都有出色的表现,该文提出基于神经网络的语义选择限制知识获取模型,设计了引入预训练词向量的单隐层前馈网络和两层maxout网络。在汉语和英语的伪消歧实验中神经网络模型取得了较好的效果,优于基于隐含狄利克雷分配的模型。

Selectional preference describes the semantic preference of the predicate for its arguments. It is an important lexical knowledge for the syntactic and semantic analysis of natural languages. Neural network models have achieved state-of-the-art performance in many natural language processing tasks. This paper deploys neural network models for selectional preference acquisition, including a one-hidden-layer feedforward network with pre-trained word vectors and a maxout network. In the pseudo-disambiguation experiments on Chinese and English, neural network models both outperform a LDA-based selectional preference acquisition model.