English and Chinese Neural Metonymy Recognition Based on Semantic Priority Interruption Theory

Metonymy is one of the types of common figurative languages and often used in human conversation without any difficulties. However, metonymy recognition in NLP requires a deep semantic/contextual processing to interpretation because it is highly related to the discourse of the contexts. Moreover, the fact that few available datasets of figurative languages make it more problematic. Motivated by the shortcomings of metonymy recognition, we develop several new data sets, including the Chinese version of the data, and design an end-to-end neural network metonymy recognizer. Our framework is based on the semantic priority interrupt theory and additional knowledge is introduced which makes to learn contexts effectively. Through a series of experiments, we show that our method is comparable to the state-of-the-art metonymy recognition method, especially we verified that metonymy trigger words information contributes to performance improvement in our model.