Multilingual dependency learning: a huge feature engineering method to semantic dependency parsing

Authors:
Hai Zhao;Wenliang Chen;Chunyu Kit;Guodong Zhou
Affiliations:
City University of Hong Kong, Kowloon, Hong Kong, China and Soochow University, Suzhou, China;National Institute of Information and Communications Technology, Soraku-gun, Kyoto, Japan;City University of Hong Kong, Kowloon, Hong Kong, China;Soochow University, Suzhou, China
Venue:
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Year:
2009

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Abstract

This paper describes our system about multilingual 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 implemented 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.