Multilingual Dependency Learning: A Huge Feature Engineering Method to Semantic Dependency Parsing ∗

This paper describes our system about mul-tilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and im-plemented as a single-stage machine learning system. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The system achieved the top average score in the closed challenge: 80.47 % semantic labeled F1 for the average score.