Refining grammars for parsing with hierarchical semantic knowledge

This paper proposes a novel method to refine the grammars in parsing by utiliz-ing semantic knowledge from HowNet. Based on the hierarchical state-split ap-proach, which can refine grammars au-tomatically in a data-driven manner, this study introduces semantic knowledge into the splitting process at two steps. Firstly, each part-of-speech node will be anno-tated with a semantic tag of its termi-nal word. These new tags generated in this step are semantic-related, which can provide a good start for splitting. Sec-ondly, a knowledge-based criterion is used to supervise the hierarchical splitting of these semantic-related tags, which can al-leviate overfitting. The experiments are carried out on both Chinese and English Penn Treebank show that the refined gram-mars with semantic knowledge can im-prove parsing performance significantly. Especially with respect to Chinese, our parser achieves an F1 score of 87.5%, which is the best published result we are aware of.