Automatic Chinese Nominal Compound Interpretation Based on Deep Neural Networks Combined with Semantic Features

摘要

The present paper reports on the results of the automatic interpretation of Chinese nominal compounds using CNN-Highway network model combined with semantic features. Chinese nominal compound interpretation is aimed to identify semantic relations between verbal nouns like "采集处理" (data acquisition and processing), and "污染处理"(wastewater treatment). The main idea is to define a set of semantic relations of verbal nouns and use deep neural network classifier with semantic features to automatically assign semantic relations to nominal compounds. Experiment shows that our model achieves 84% Fl-score on the test dataset. Convolutional layer plus highway network combined with semantic features architecture can effectively solve the problem of Chinese nominal compound interpretation.