Refining Grammars for Parsing with Hierarchical Semantic Knowledge

This paper proposes a novel method to refine the grammars in parsing by utilizing semantic knowledge from HowNet. Based on the hierarchical state-split approach, which can refine grammars automatically 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 annotated with a semantic tag of its terminal word. These new tags generated in this step are semantic-related, which can provide a good start for splitting. Secondly, a knowledge-based criterion is used to supervise the hierarchical splitting of these semantic-related tags, which can alleviate overfitting. The experiments are carried out on both Chinese and English Penn Treebank show that the refined grammars with semantic knowledge can improve 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.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Xiaojun Lin, Yang Fan, Meng Zhang, Xihong Wu, Huisheng Chi
• Keywords: EMNLP 2009, Grammars, Hierarchical State-split Approach, Natural Language Processing, Semantic Knowledge